MERDs Data Analyses
Lukas Bell-Dereske
8/27/2020
Import dataset into R from your directory Here is my directory
SEED GERMINATION PORTION OF THE EXPERIEMENT BEGINS
Time to first germination in a given pot
Full Statistical model
Diagnostic Graphs
day_t0_germ_model= lmer((t0_germ)^-1~precip*soil_root+(1|block), data= dataSG_seed_1st_germ_trt)
qqPlot(resid(day_t0_germ_model))
## 3 85
## 2 56hist(resid(day_t0_germ_model))
shapiro.test(resid(day_t0_germ_model))##
## Shapiro-Wilk normality test
##
## data: resid(day_t0_germ_model)
## W = 0.96991, p-value = 0.1514Raw Statistical output
anova(day_t0_germ_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 0.0119463 0.0119463 1 50.215 3.5356 0.06587 .
## soil_root 0.0313006 0.0156503 2 50.655 4.6318 0.01421 *
## precip:soil_root 0.0045726 0.0022863 2 51.349 0.6766 0.51279
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(day_t0_germ_model, pairwise~soil_root)## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect## NOTE: Results may be misleading due to involvement in interactions## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.195 0.0148 11.9 0.162 0.227
## S.B 0.140 0.0176 21.6 0.103 0.176
## L.R 0.195 0.0150 12.7 0.163 0.228
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.054728 0.0201 49.2 2.726 0.0236
## L.B - L.R -0.000469 0.0183 51.3 -0.026 0.9996
## S.B - L.R -0.055197 0.0204 50.1 -2.702 0.0250
##
## Results are averaged over the levels of: precip
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimatesemmeans(day_t0_germ_model, pairwise~soil_root|precip)## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.187 0.0186 24.8 0.1488 0.226
## S.B 0.111 0.0223 35.9 0.0656 0.156
## L.R 0.187 0.0168 19.7 0.1519 0.222
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.202 0.0202 29.5 0.1608 0.243
## S.B 0.169 0.0254 42.2 0.1178 0.220
## L.R 0.203 0.0225 34.9 0.1576 0.249
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.076424 0.0272 51.2 2.807 0.0190
## L.B - L.R 0.000133 0.0227 49.8 0.006 1.0000
## S.B - L.R -0.076292 0.0257 50.0 -2.964 0.0127
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.033031 0.0303 49.8 1.091 0.5241
## L.B - L.R -0.001071 0.0283 51.2 -0.038 0.9992
## S.B - L.R -0.034101 0.0325 51.9 -1.048 0.5504
##
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimatesFormatted Anova table
| Df | Resid.Df | F.value | P.value | |
|---|---|---|---|---|
| precip | 1 | 50.22 | 3.54 | 0.066 |
| soil_root | 2 | 50.66 | 4.63 | 0.014 |
| precip:soil_root | 2 | 51.35 | 0.68 | 0.513 |
Graph for publication
## Warning: `funs()` is deprecated as of dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
Presence Days to first Germination
Diagnostic Graphs
dataSG_seed_1st_germ_pres=subset(dataSG_seed_1st_germ_trt, root_association =="B")
unique(dataSG_seed_1st_germ_pres$soil_root)## [1] L.B S.B
## Levels: L.B S.B L.R S.Rnrow(dataSG_seed_1st_germ_pres)## [1] 60day_t0_germ_model_pres= lmer((t0_germ)^-1~precip*soil_root+(1|block), data= dataSG_seed_1st_germ_pres)
qqPlot(resid(day_t0_germ_model_pres))
## 85 24
## 33 6hist(resid(day_t0_germ_model_pres))
shapiro.test(resid(day_t0_germ_model_pres))##
## Shapiro-Wilk normality test
##
## data: resid(day_t0_germ_model_pres)
## W = 0.98241, p-value = 0.8234Raw Statistical output
anova(day_t0_germ_model_pres)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 0.0122308 0.0122308 1 29.562 4.2813 0.047376 *
## soil_root 0.0252094 0.0252094 1 28.581 8.8243 0.005968 **
## precip:soil_root 0.0052936 0.0052936 1 30.328 1.8530 0.183466
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(day_t0_germ_model_pres, pairwise~soil_root)## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect## NOTE: Results may be misleading due to involvement in interactions## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.196 0.0168 5.72 0.1546 0.238
## S.B 0.141 0.0188 9.08 0.0987 0.184
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.0548 0.0185 28.2 2.966 0.0061
##
## Results are averaged over the levels of: precip
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-rogeremmeans(day_t0_germ_model_pres, pairwise~soil_root|precip)## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.190 0.0197 10.4 0.1459 0.233
## S.B 0.109 0.0227 15.8 0.0607 0.157
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.203 0.0211 12.6 0.1569 0.248
## S.B 0.174 0.0256 19.9 0.1201 0.227
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.0807 0.0255 29.9 3.161 0.0036
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.0290 0.0280 28.7 1.033 0.3103
##
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-rogerFormatted Anova table
| Df | Resid.Df | F.value | P.value | |
|---|---|---|---|---|
| precip | 1 | 29.56 | 4.28 | 0.047 |
| soil_root | 1 | 28.58 | 8.82 | 0.006 |
| precip:soil_root | 1 | 30.33 | 1.85 | 0.183 |
Origin Days to first Germination
Diagnostic Graphs
dataSG_seed_1st_germ_orig=subset(dataSG_seed_1st_germ_trt, soil_status =="L")
unique(dataSG_seed_1st_germ_orig$soil_root)## [1] L.R L.B
## Levels: L.B S.B L.R S.Rnrow(dataSG_seed_1st_germ_orig)## [1] 60#60
day_t0_germ_model_orig= lmer((t0_germ)^-2~precip*soil_root+(1|block), data= dataSG_seed_1st_germ_orig)
qqPlot(resid(day_t0_germ_model_orig))
## 3 66
## 2 37hist(resid(day_t0_germ_model_orig))
shapiro.test(resid(day_t0_germ_model_orig))##
## Shapiro-Wilk normality test
##
## data: resid(day_t0_germ_model_orig)
## W = 0.93385, p-value = 0.01286#0.01286Raw Statistical output
anova(day_t0_germ_model_orig)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 0.00040305 0.00040305 1 39.847 1.0708 0.3070
## soil_root 0.00001075 0.00001075 1 40.258 0.0285 0.8667
## precip:soil_root 0.00000263 0.00000263 1 38.791 0.0070 0.9339#emmeans(day_t0_germ_model_orig, pairwise~soil_root)
#emmeans(day_t0_germ_model_orig, pairwise~soil_root|precip)Formatted Anova table
| Df | Resid.Df | F.value | P.value | |
|---|---|---|---|---|
| precip | 1 | 39.85 | 1.07 | 0.307 |
| soil_root | 1 | 40.26 | 0.03 | 0.867 |
| precip:soil_root | 1 | 38.79 | 0.01 | 0.934 |
Final number of germinates
Diagnostic Graphs
#Total germination
#convert to percentages
fin_dataSG_seed_surv_trt_tot_seedling$prop_tot_num_germ=fin_dataSG_seed_surv_trt_tot_seedling$tot_num_germ/10
seed_germ_model= glmer.nb((tot_num_germ)~precip*soil_root+(1|block), data= fin_dataSG_seed_surv_trt_tot_seedling)## Warning in theta.ml(Y, mu, weights = object@resp$weights, limit = limit, :
## iteration limit reached## boundary (singular) fit: see ?isSingularqqPlot(resid(seed_germ_model))
## 37 42
## 59 61hist(resid(seed_germ_model))
shapiro.test(resid(seed_germ_model))##
## Shapiro-Wilk normality test
##
## data: resid(seed_germ_model)
## W = 0.93147, p-value = 0.0001422#p-value = 0.0001422
plot(seed_germ_model)
#Chad's code for checking for over dispersion
overdisp_fun <- function(model) {
vpars <- function(m) {
nrow(m)*(nrow(m)+1)/2
}
model.df <- sum(sapply(VarCorr(model),vpars))+length(fixef(model))
rdf <- nrow(model.frame(model))-model.df
rp <- residuals(model,type="pearson")
Pearson.chisq <- sum(rp^2)
prat <- Pearson.chisq/rdf
pval <- pchisq(Pearson.chisq, df=rdf, lower.tail=FALSE)
c(chisq=Pearson.chisq,ratio=prat,rdf=rdf,p=pval)
}
overdisp_fun(seed_germ_model)## chisq ratio rdf p
## 61.5017682 0.7409852 83.0000000 0.9629765Raw Statistical output
summary(seed_germ_model)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Negative Binomial(54342.88) ( log )
## Formula: (tot_num_germ) ~ precip * soil_root + (1 | block)
## Data: fin_dataSG_seed_surv_trt_tot_seedling
##
## AIC BIC logLik deviance df.resid
## 222.9 242.9 -103.5 206.9 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.12545 -0.73029 -0.08396 0.40823 2.00830
##
## Random effects:
## Groups Name Variance Std.Dev.
## block (Intercept) 5.981e-11 7.733e-06
## Number of obs: 90, groups: block, 5
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.12688 0.11619 -1.092 0.2748
## precip1 0.24080 0.11618 2.073 0.0382 *
## soil_root1 0.21058 0.15408 1.367 0.1717
## soil_root2 -0.39015 0.17898 -2.180 0.0293 *
## precip1:soil_root1 -0.08811 0.15408 -0.572 0.5674
## precip1:soil_root2 -0.12923 0.17898 -0.722 0.4703
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) precp1 sl_rt1 sl_rt2 pr1:_1
## precip1 -0.211
## soil_root1 -0.178 0.072
## soil_root2 0.245 0.039 -0.556
## prcp1:sl_r1 0.072 -0.178 -0.185 0.027
## prcp1:sl_r2 0.039 0.245 0.027 -0.153 -0.556
## convergence code: 0
## boundary (singular) fit: see ?isSingularAnova(seed_germ_model,type = 3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: (tot_num_germ)
## Chisq Df Pr(>Chisq)
## (Intercept) 1.1925 1 0.27483
## precip 4.2959 1 0.03821 *
## soil_root 4.7863 2 0.09134 .
## precip:soil_root 1.8911 2 0.38847
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(seed_germ_model,pairwise~soil_root|precip)## $emmeans
## precip = A:
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B 0.236 0.229 Inf -0.212 0.6849
## S.B -0.405 0.315 Inf -1.023 0.2119
## L.R 0.511 0.200 Inf 0.120 0.9021
##
## precip = D:
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B -0.069 0.267 Inf -0.592 0.4535
## S.B -0.629 0.352 Inf -1.318 0.0609
## L.R -0.405 0.315 Inf -1.023 0.2122
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df z.ratio p.value
## L.B - S.B 0.642 0.389 Inf 1.650 0.2248
## L.B - L.R -0.274 0.304 Inf -0.903 0.6381
## S.B - L.R -0.916 0.373 Inf -2.457 0.0373
##
## precip = D:
## contrast estimate SE df z.ratio p.value
## L.B - S.B 0.560 0.441 Inf 1.269 0.4126
## L.B - L.R 0.336 0.413 Inf 0.815 0.6938
## S.B - L.R -0.223 0.472 Inf -0.472 0.8843
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesFormatted Anova table
| Df | Chi.Sq | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 1.19 | 0.275 |
| precip | 1 | 4.30 | 0.038 |
| soil_root | 2 | 4.79 | 0.091 |
| precip:soil_root | 2 | 1.89 | 0.388 |
Presence Number of Seeds that Geminated
Diagnostic Graphs
fin_dataSG_seed_surv_trt_tot_seedling_pres=subset(fin_dataSG_seed_surv_trt_tot_seedling, root_association =="B")
seed_germ_model_pres= glmer.nb((tot_num_germ)~precip*soil_root+(1|block), data= fin_dataSG_seed_surv_trt_tot_seedling_pres)## Warning in theta.ml(Y, mu, weights = object@resp$weights, limit = limit, :
## iteration limit reachedqqPlot(resid(seed_germ_model_pres))
## 42 37
## 43 41hist(resid(seed_germ_model_pres))
shapiro.test(resid(seed_germ_model_pres))##
## Shapiro-Wilk normality test
##
## data: resid(seed_germ_model_pres)
## W = 0.92034, p-value = 0.0007916#p-value = 0.0007916
plot(seed_germ_model_pres)
Raw Statistical output
summary(seed_germ_model_pres)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Negative Binomial(52724.35) ( log )
## Formula: (tot_num_germ) ~ precip * soil_root + (1 | block)
## Data: fin_dataSG_seed_surv_trt_tot_seedling_pres
##
## AIC BIC logLik deviance df.resid
## 146.5 159.1 -67.3 134.5 54
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.14275 -0.76619 -0.07703 0.58158 1.95560
##
## Random effects:
## Groups Name Variance Std.Dev.
## block (Intercept) 0.03097 0.176
## Number of obs: 60, groups: block, 5
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.23211 0.17146 -1.354 0.1758
## precip1 0.13213 0.14743 0.896 0.3701
## soil_root1 0.30036 0.14750 2.036 0.0417 *
## precip1:soil_root1 0.02056 0.14753 0.139 0.8892
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) precp1 sl_rt1
## precip1 -0.106
## soil_root1 -0.246 0.018
## prcp1:sl_r1 0.015 -0.289 -0.126Anova(seed_germ_model_pres,type = 3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: (tot_num_germ)
## Chisq Df Pr(>Chisq)
## (Intercept) 1.8325 1 0.17583
## precip 0.8033 1 0.37012
## soil_root 4.1466 1 0.04172 *
## precip:soil_root 0.0194 1 0.88918
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(seed_germ_model_pres,pairwise~soil_root|precip)## $emmeans
## precip = A:
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B 0.2209 0.246 Inf -0.261 0.7024
## S.B -0.4209 0.327 Inf -1.063 0.2210
##
## precip = D:
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B -0.0844 0.281 Inf -0.635 0.4664
## S.B -0.6440 0.363 Inf -1.356 0.0681
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df z.ratio p.value
## L.B - S.B 0.642 0.390 Inf 1.645 0.0999
##
## precip = D:
## contrast estimate SE df z.ratio p.value
## L.B - S.B 0.560 0.443 Inf 1.264 0.2062
##
## Results are given on the log (not the response) scale.Formatted Anova table
| Df | Chi.Sq | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 1.83 | 0.176 |
| precip | 1 | 0.80 | 0.370 |
| soil_root | 1 | 4.15 | 0.042 |
| precip:soil_root | 1 | 0.02 | 0.889 |
Origin Number of Seeds that Geminated
Diagnostic Graphs
fin_dataSG_seed_surv_trt_tot_seedling_org=subset(fin_dataSG_seed_surv_trt_tot_seedling, soil_status =="L")
seed_germ_model_org= glmer.nb((tot_num_germ)~precip*soil_root+(1|block), data= fin_dataSG_seed_surv_trt_tot_seedling_org)## Warning in theta.ml(Y, mu, weights = object@resp$weights, limit = limit, :
## iteration limit reached## boundary (singular) fit: see ?isSingularqqPlot(resid(seed_germ_model_org))
## 58 6
## 49 26hist(resid(seed_germ_model_org))
shapiro.test(resid(seed_germ_model_org))##
## Shapiro-Wilk normality test
##
## data: resid(seed_germ_model_org)
## W = 0.95425, p-value = 0.02477#p-value = 0.02477
plot(seed_germ_model_org)
Raw Statistical output
summary(seed_germ_model_org)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Negative Binomial(77187.13) ( log )
## Formula: (tot_num_germ) ~ precip * soil_root + (1 | block)
## Data: fin_dataSG_seed_surv_trt_tot_seedling_org
##
## AIC BIC logLik deviance df.resid
## 159.2 171.8 -73.6 147.2 54
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.12545 -0.59142 0.06902 0.40825 1.80738
##
## Random effects:
## Groups Name Variance Std.Dev.
## block (Intercept) 0 0
## Number of obs: 60, groups: block, 5
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.06818 0.12820 0.532 0.5948
## precip1 0.30542 0.12820 2.382 0.0172 *
## soil_root1 0.01551 0.12820 0.121 0.9037
## precip1:soil_root1 -0.15272 0.12820 -1.191 0.2335
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) precp1 sl_rt1
## precip1 -0.297
## soil_root1 -0.064 0.158
## prcp1:sl_r1 0.158 -0.064 -0.297
## convergence code: 0
## boundary (singular) fit: see ?isSingularAnova(seed_germ_model_org,type = 3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: (tot_num_germ)
## Chisq Df Pr(>Chisq)
## (Intercept) 0.2829 1 0.5948
## precip 5.6755 1 0.0172 *
## soil_root 0.0146 1 0.9037
## precip:soil_root 1.4191 1 0.2335
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(seed_germ_model_org,pairwise~soil_root|precip)## $emmeans
## precip = A:
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B 0.236 0.229 Inf -0.213 0.685
## L.R 0.511 0.200 Inf 0.119 0.903
##
## precip = D:
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B -0.069 0.266 Inf -0.590 0.452
## L.R -0.405 0.316 Inf -1.025 0.214
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df z.ratio p.value
## L.B - L.R -0.274 0.304 Inf -0.902 0.3668
##
## precip = D:
## contrast estimate SE df z.ratio p.value
## L.B - L.R 0.336 0.413 Inf 0.815 0.4152
##
## Results are given on the log (not the response) scale.Formatted Anova table
| Df | Chi.Sq | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 0.28 | 0.59 |
| precip | 1 | 5.68 | 0.02 |
| soil_root | 1 | 0.01 | 0.90 |
| precip:soil_root | 1 | 1.42 | 0.23 |
Graph for publication 
## Warning: package 'gdtools' was built under R version 4.0.2Survival given germination
Full Statistical model
Diagnostic Graphs
seed_surv_germ_model= glmer(surv_germ~precip*soil_root+(1|block), data= fin_dataSG_biomass_seed_surv_trt_w_germ, family="binomial")
qqPlot(resid(seed_surv_germ_model))
## 24 71
## 14 47hist(resid(seed_surv_germ_model))
shapiro.test(resid(seed_surv_germ_model))##
## Shapiro-Wilk normality test
##
## data: resid(seed_surv_germ_model)
## W = 0.79433, p-value = 1.174e-07#1.174e-07
plot(seed_surv_germ_model)
Raw Statistical output
Anova(seed_surv_germ_model, type=3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: surv_germ
## Chisq Df Pr(>Chisq)
## (Intercept) 4.6354 1 0.03132 *
## precip 0.0623 1 0.80290
## soil_root 7.0596 2 0.02931 *
## precip:soil_root 2.3638 2 0.30669
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#soil_root 9.8880 2 0.007126 **
#soil_root 7.0596 2 0.02931 *
#precip:soil_root 4.3070 2 0.116077
emmeans(seed_surv_germ_model, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B 1.946 0.813 Inf 0.353 3.54
## S.B -0.243 0.746 Inf -1.705 1.22
## L.R 2.015 0.850 Inf 0.350 3.68
##
## Results are averaged over the levels of: precip
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## L.B - S.B 2.1897 0.942 Inf 2.324 0.0526
## L.B - L.R -0.0682 0.977 Inf -0.070 0.9973
## S.B - L.R -2.2579 0.996 Inf -2.266 0.0606
##
## Results are averaged over the levels of: precip
## Results are given on the log odds ratio (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesemmeans(seed_surv_germ_model, pairwise~soil_root|precip)## $emmeans
## precip = A:
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B 1.644 0.928 Inf -0.1741 3.462
## S.B 0.731 0.929 Inf -1.0904 2.552
## L.R 1.629 0.846 Inf -0.0287 3.287
##
## precip = D:
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B 2.249 1.187 Inf -0.0779 4.576
## S.B -1.217 1.087 Inf -3.3480 0.913
## L.R 2.400 1.270 Inf -0.0886 4.888
##
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df z.ratio p.value
## L.B - S.B 0.9133 1.16 Inf 0.787 0.7110
## L.B - L.R 0.0146 1.08 Inf 0.013 0.9999
## S.B - L.R -0.8987 1.06 Inf -0.845 0.6748
##
## precip = D:
## contrast estimate SE df z.ratio p.value
## L.B - S.B 3.4662 1.53 Inf 2.259 0.0617
## L.B - L.R -0.1510 1.59 Inf -0.095 0.9951
## S.B - L.R -3.6172 1.67 Inf -2.168 0.0767
##
## Results are given on the log odds ratio (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesFormatted Anova table
| Df | Chi.Sq | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 4.64 | 0.031 |
| precip | 1 | 0.06 | 0.803 |
| soil_root | 2 | 7.06 | 0.029 |
| precip:soil_root | 2 | 2.36 | 0.307 |
| Graph for publicati | on | ||
| <img src="MERDS_R_c | ode_SG | _reAnalysi | s_20200828_files/figure-html/unnamed-chunk-29-1.png" width=“672” /> |
Presence survival given germination
Diagnostic Graphs
fin_dataSG_biomass_seed_surv_pres_w_germ=subset(fin_dataSG_biomass_seed_surv_trt_w_germ, root_association =="B")
seed_surv_germ_model_pres= glmer(surv_germ~precip*soil_root+(1|block), data= fin_dataSG_biomass_seed_surv_pres_w_germ, family="binomial")## boundary (singular) fit: see ?isSingularqqPlot(resid(seed_surv_germ_model_pres))
## 24 71
## 6 24hist(resid(seed_surv_germ_model_pres))
shapiro.test(resid(seed_surv_germ_model_pres))##
## Shapiro-Wilk normality test
##
## data: resid(seed_surv_germ_model_pres)
## W = 0.81516, p-value = 3.305e-05#3.305e-05
plot(seed_surv_germ_model_pres)
Raw Statistical output
Anova(seed_surv_germ_model_pres, type=3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: surv_germ
## Chisq Df Pr(>Chisq)
## (Intercept) 4.3867 1 0.03622 *
## precip 0.1268 1 0.72178
## soil_root 5.3138 1 0.02116 *
## precip:soil_root 1.0721 1 0.30047
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(seed_surv_germ_model_pres, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B 1.9033 0.654 Inf 0.621 3.19
## S.B -0.0912 0.566 Inf -1.201 1.02
##
## Results are averaged over the levels of: precip
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## L.B - S.B 1.99 0.865 Inf 2.305 0.0212
##
## Results are averaged over the levels of: precip
## Results are given on the log odds ratio (not the response) scale.emmeans(seed_surv_germ_model_pres, pairwise~soil_root|precip)## $emmeans
## precip = A:
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B 1.609 0.775 Inf 0.0913 3.13
## S.B 0.511 0.730 Inf -0.9205 1.94
##
## precip = D:
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B 2.197 1.054 Inf 0.1312 4.26
## S.B -0.693 0.866 Inf -2.3905 1.00
##
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df z.ratio p.value
## L.B - S.B 1.10 1.06 Inf 1.032 0.3021
##
## precip = D:
## contrast estimate SE df z.ratio p.value
## L.B - S.B 2.89 1.36 Inf 2.119 0.0341
##
## Results are given on the log odds ratio (not the response) scale.Formatted Anova table
| Df | Chi.Sq | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 4.39 | 0.036 |
| precip | 1 | 0.13 | 0.722 |
| soil_root | 1 | 5.31 | 0.021 |
| precip:soil_root | 1 | 1.07 | 0.300 |
Origin survival given germination
Diagnostic Graphs
fin_dataSG_biomass_seed_surv_orig_w_germ=subset(fin_dataSG_biomass_seed_surv_trt_w_germ, soil_status =="L")
seed_surv_germ_model_orig= glmer(surv_germ~precip*soil_root+(1|block), data= fin_dataSG_biomass_seed_surv_orig_w_germ, family="binomial")
qqPlot(resid(seed_surv_germ_model_orig))
## 24 71
## 14 41hist(resid(seed_surv_germ_model_orig))
shapiro.test(resid(seed_surv_germ_model_orig))##
## Shapiro-Wilk normality test
##
## data: resid(seed_surv_germ_model_orig)
## W = 0.54238, p-value = 1.23e-10#1.23e-10
plot(seed_surv_germ_model_orig)
Raw Statistical output
Anova(seed_surv_germ_model_orig, type=3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: surv_germ
## Chisq Df Pr(>Chisq)
## (Intercept) 11.8503 1 0.0005765 ***
## precip 0.3965 1 0.5288941
## soil_root 0.0390 1 0.8433818
## precip:soil_root 0.0001 1 0.9942924
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(seed_surv_germ_model_orig, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B 1.92 0.700 Inf 0.551 3.30
## L.R 1.74 0.714 Inf 0.341 3.14
##
## Results are averaged over the levels of: precip
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## L.B - L.R 0.184 0.931 Inf 0.198 0.8434
##
## Results are averaged over the levels of: precip
## Results are given on the log odds ratio (not the response) scale.emmeans(seed_surv_germ_model_orig, pairwise~soil_root|precip)## $emmeans
## precip = A:
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B 1.63 0.819 Inf 0.0263 3.24
## L.R 1.45 0.724 Inf 0.0350 2.87
##
## precip = D:
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B 2.22 1.086 Inf 0.0865 4.34
## L.R 2.03 1.139 Inf -0.2070 4.26
##
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df z.ratio p.value
## L.B - L.R 0.177 1.03 Inf 0.172 0.8634
##
## precip = D:
## contrast estimate SE df z.ratio p.value
## L.B - L.R 0.190 1.53 Inf 0.125 0.9009
##
## Results are given on the log odds ratio (not the response) scale.Formatted Anova table
| Df | Chi.Sq | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 11.85 | 0.001 |
| precip | 1 | 0.40 | 0.529 |
| soil_root | 1 | 0.04 | 0.843 |
| precip:soil_root | 1 | 0.00 | 0.994 |
Biomass given germination
Diagnostic Graphs
seed_biomas_model= lmer(log(total_biomass)~precip*soil_root+(1|block), data= fin_dataSG_biomass_seed_surv_trt_given_germ)## boundary (singular) fit: see ?isSingularqqPlot(resid(seed_biomas_model))
## 12 52
## 6 23hist(resid(seed_biomas_model))
shapiro.test(resid(seed_biomas_model))##
## Shapiro-Wilk normality test
##
## data: resid(seed_biomas_model)
## W = 0.9757, p-value = 0.457#p-value = 0.457Raw Statistical output
anova(seed_biomas_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 0.7614 0.7614 1 39 0.8395 0.3651603
## soil_root 7.2196 3.6098 2 39 3.9802 0.0267300 *
## precip:soil_root 18.1525 9.0763 2 39 10.0075 0.0003104 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(seed_biomas_model, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B -3.03 0.222 14.6 -3.51 -2.555
## S.B -1.76 0.427 28.4 -2.63 -0.885
## L.R -2.85 0.237 14.2 -3.36 -2.342
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - S.B -1.271 0.475 38.9 -2.677 0.0285
## L.B - L.R -0.181 0.322 37.5 -0.563 0.8404
## S.B - L.R 1.089 0.495 38.0 2.201 0.0839
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesemmeans(seed_biomas_model, pairwise~soil_root|precip)## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B -2.639 0.309 28.4 -3.27 -2.01
## S.B -3.184 0.435 37.2 -4.07 -2.30
## L.R -2.281 0.278 25.8 -2.85 -1.71
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B -3.421 0.323 31.2 -4.08 -2.76
## S.B -0.335 0.740 32.3 -1.84 1.17
## L.R -3.417 0.379 30.9 -4.19 -2.64
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.54492 0.538 38.0 1.014 0.5729
## L.B - L.R -0.35848 0.413 36.5 -0.868 0.6635
## S.B - L.R -0.90339 0.511 36.3 -1.767 0.1951
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B -3.08610 0.807 37.8 -3.822 0.0014
## L.B - L.R -0.00398 0.488 37.1 -0.008 1.0000
## S.B - L.R 3.08212 0.863 32.0 3.572 0.0032
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 39 | 0.84 | 0.365 |
| 2 | 39 | 3.98 | 0.027 |
| 2 | 39 | 10.01 | 0.000 |
| Graph | for publicat | ion | |
| <img s | rc="MERDS_R_ | code_SG_reA | nalysis_20200828_files/figure-html/unnamed-chunk-40-1.png" width=“672” /> |
Presence total biomass of germinate
Diagnostic Graphs
fin_dataSG_biomass_seed_surv_pres_w_germ=subset(fin_dataSG_biomass_seed_surv_trt_given_germ, root_association =="B")
nrow(fin_dataSG_biomass_seed_surv_pres_w_germ)## [1] 26#26
unique(fin_dataSG_biomass_seed_surv_pres_w_germ$soil_root)## [1] L.B S.B
## Levels: L.B S.B L.R S.Rseed_biomas_model_pres= lmer((total_biomass)~precip*soil_root+(1|block), data= fin_dataSG_biomass_seed_surv_pres_w_germ)## boundary (singular) fit: see ?isSingularqqPlot(resid(seed_biomas_model_pres))
## 25 75
## 6 21hist(resid(seed_biomas_model_pres))
shapiro.test(resid(seed_biomas_model_pres))##
## Shapiro-Wilk normality test
##
## data: resid(seed_biomas_model_pres)
## W = 0.93832, p-value = 0.1224#p-value = 0.1224Raw Statistical output
anova(seed_biomas_model_pres)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 0.43448 0.43448 1 22 147.58 3.139e-11 ***
## soil_root 0.44799 0.44799 1 22 152.17 2.336e-11 ***
## precip:soil_root 0.55107 0.55107 1 22 187.19 3.080e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(seed_biomas_model_pres, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.0664 0.0129 5.22 0.0338 0.099
## S.B 0.3859 0.0262 12.75 0.3291 0.443
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - S.B -0.319 0.0284 21.9 -11.252 <.0001
##
## Results are averaged over the levels of: precip
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-rogeremmeans(seed_biomas_model_pres, pairwise~soil_root|precip)## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.0863 0.0180 12.8 0.047359 0.1252
## S.B 0.0514 0.0251 20.1 -0.000969 0.1038
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.0466 0.0187 14.8 0.006715 0.0864
## S.B 0.7203 0.0466 15.7 0.621443 0.8192
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.0348 0.0313 20.9 1.114 0.2780
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B -0.6737 0.0501 20.7 -13.448 <.0001
##
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-rogerFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 22 | 147.58 | 0 |
| 1 | 22 | 152.17 | 0 |
| 1 | 22 | 187.19 | 0 |
| ### Or | igin total b | iomass of g | erminated |
Diagnostic Graphs
fin_dataSG_biomass_seed_surv_orig_w_germ=subset(fin_dataSG_biomass_seed_surv_trt_given_germ, soil_status =="L")
nrow(fin_dataSG_biomass_seed_surv_orig_w_germ)## [1] 38#38
unique(fin_dataSG_biomass_seed_surv_orig_w_germ$soil_root)## [1] L.R L.B
## Levels: L.B S.B L.R S.Rseed_biomas_model_orig= lmer(log(total_biomass)~precip*soil_root+(1|block), data= fin_dataSG_biomass_seed_surv_orig_w_germ)## boundary (singular) fit: see ?isSingularqqPlot(resid(seed_biomas_model_orig))
## 12 52
## 6 21hist(resid(seed_biomas_model_orig))
shapiro.test(resid(seed_biomas_model_orig))##
## Shapiro-Wilk normality test
##
## data: resid(seed_biomas_model_orig)
## W = 0.97636, p-value = 0.5891#p-value = 0.5891Raw Statistical output
anova(seed_biomas_model_orig)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 8.4164 8.4164 1 34 8.7072 0.005705 **
## soil_root 0.3004 0.3004 1 34 0.3108 0.580839
## precip:soil_root 0.2874 0.2874 1 34 0.2973 0.589143
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(seed_biomas_model_orig, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B -3.03 0.230 11.8 -3.53 -2.53
## L.R -2.85 0.246 11.4 -3.39 -2.31
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - L.R -0.181 0.333 32.4 -0.544 0.5901
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.emmeans(seed_biomas_model_orig, pairwise~soil_root|precip)## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B -2.64 0.320 24.0 -3.30 -1.98
## L.R -2.28 0.288 21.6 -2.88 -1.68
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B -3.42 0.335 26.6 -4.11 -2.73
## L.R -3.42 0.393 26.3 -4.23 -2.61
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - L.R -0.35848 0.427 31.4 -0.840 0.4074
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - L.R -0.00398 0.505 32.1 -0.008 0.9938
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.Formatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 34 | 8.71 | 0.006 |
| 1 | 34 | 0.31 | 0.581 |
| 1 | 34 | 0.30 | 0.589 |
Root:shoot ratio given germination
Diagnostic Graphs
#now root:shoot ratio
fin_dataSG_biomass_seed_surv_trt_given_germ$root_shoot=fin_dataSG_biomass_seed_surv_trt_given_germ$root_weight_g/fin_dataSG_biomass_seed_surv_trt_given_germ$shoot_weight_g
summary(fin_dataSG_biomass_seed_surv_trt_given_germ)## Plant_Number date Rdate exp_days
## Min. : 2.00 Length:45 Length:45 Min. :27
## 1st Qu.:23.00 Class :character Class :character 1st Qu.:27
## Median :52.00 Mode :character Mode :character Median :27
## Mean :46.49 Mean :27
## 3rd Qu.:66.00 3rd Qu.:27
## Max. :89.00 Max. :27
## num_germinates volunteer_dicot num_removed raw_label
## Min. :0.0000 Min. :0 Min. :0 Length:45
## 1st Qu.:1.0000 1st Qu.:0 1st Qu.:0 Class :character
## Median :1.0000 Median :0 Median :0 Mode :character
## Mean :0.9778 Mean :0 Mean :0
## 3rd Qu.:1.0000 3rd Qu.:0 3rd Qu.:0
## Max. :1.0000 Max. :0 Max. :0
## soil_status root_association block precip
## Length:45 Length:45 Min. :1.000 Length:45
## Class :character Class :character 1st Qu.:2.000 Class :character
## Mode :character Mode :character Median :3.000 Mode :character
## Mean :3.156
## 3rd Qu.:4.000
## Max. :5.000
## life_stage trt pot_w_germ soil_root
## Length:45 Length:45 Min. :0.0000 L.B:19
## Class :character Class :character 1st Qu.:1.0000 S.B: 7
## Mode :character Mode :character Median :1.0000 L.R:19
## Mean :0.9778 S.R: 0
## 3rd Qu.:1.0000
## Max. :1.0000
## tot_num_germ_harvest t0_germ germinated root_weight_g
## Min. :0.000 Min. : 4.000 Min. :1 Min. :0.0021
## 1st Qu.:1.000 1st Qu.: 4.000 1st Qu.:1 1st Qu.:0.0243
## Median :1.000 Median : 5.000 Median :1 Median :0.0507
## Mean :1.556 Mean : 6.044 Mean :1 Mean :0.1223
## 3rd Qu.:2.000 3rd Qu.: 6.000 3rd Qu.:1 3rd Qu.:0.1022
## Max. :4.000 Max. :19.000 Max. :1 Max. :1.1619
## shoot_weight_g total_biomass notes_roots notes_shoots
## Min. :0.00050 Min. :0.0026 Length:45 Length:45
## 1st Qu.:0.00360 1st Qu.:0.0294 Class :character Class :character
## Median :0.00640 Median :0.0612 Mode :character Mode :character
## Mean :0.00812 Mean :0.1305
## 3rd Qu.:0.01130 3rd Qu.:0.1116
## Max. :0.02560 Max. :1.1875
## sandy_root surv_germ root_shoot
## Length:45 Min. :1 Min. : 2.303
## Class :character 1st Qu.:1 1st Qu.: 4.168
## Mode :character Median :1 Median : 7.306
## Mean :1 Mean :14.315
## 3rd Qu.:1 3rd Qu.:13.850
## Max. :1 Max. :80.657root_shoot_seed_model= lmer(log(root_shoot)~precip*soil_root+(1|block), data= fin_dataSG_biomass_seed_surv_trt_given_germ)
qqPlot(resid(root_shoot_seed_model))
## 51 52
## 22 23hist(resid(root_shoot_seed_model))
shapiro.test(resid(root_shoot_seed_model))##
## Shapiro-Wilk normality test
##
## data: resid(root_shoot_seed_model)
## W = 0.97031, p-value = 0.2972#0.2972Raw Statistical output
anova(root_shoot_seed_model, type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 6.3079 6.3079 1 37.122 8.3663 0.006359 **
## soil_root 1.4238 0.7119 2 37.992 0.9442 0.397931
## precip:soil_root 3.9101 1.9550 2 38.417 2.5930 0.087835 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(root_shoot_seed_model, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B 2.06 0.229 10.5 1.55 2.57
## S.B 2.56 0.406 28.2 1.73 3.39
## L.R 2.35 0.240 11.1 1.83 2.88
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - S.B -0.504 0.432 38.0 -1.165 0.4808
## L.B - L.R -0.293 0.293 36.5 -1.002 0.5802
## S.B - L.R 0.211 0.451 39.0 0.467 0.8869
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesemmeans(root_shoot_seed_model, pairwise~soil_root|precip)## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 2.00 0.300 22.1 1.382 2.63
## S.B 1.54 0.412 34.2 0.701 2.37
## L.R 2.08 0.276 18.5 1.506 2.66
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 2.11 0.313 24.7 1.469 2.76
## S.B 3.59 0.683 36.3 2.203 4.97
## L.R 2.62 0.359 28.2 1.887 3.36
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.4664 0.487 36.8 0.957 0.6082
## L.B - L.R -0.0791 0.375 35.8 -0.211 0.9758
## S.B - L.R -0.5455 0.466 35.8 -1.171 0.4779
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B -1.4742 0.734 39.0 -2.010 0.1233
## L.B - L.R -0.5076 0.445 36.4 -1.141 0.4954
## S.B - L.R 0.9666 0.783 38.4 1.235 0.4403
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 37.12 | 8.37 | 0.006 |
| 2 | 37.99 | 0.94 | 0.398 |
| 2 | 38.42 | 2.59 | 0.088 |
Graph for publication 
Presence Root:shoot ratio given germination
Diagnostic Graphs
#now root:shoot ratio
fin_dataSG_biomass_seed_surv_pres_w_germ=subset(fin_dataSG_biomass_seed_surv_trt_given_germ, root_association =="B")
root_shoot_seed_model_pres= lmer(log(root_shoot)~precip*soil_root+(1|block), data= fin_dataSG_biomass_seed_surv_pres_w_germ)## boundary (singular) fit: see ?isSingularqqPlot(resid(root_shoot_seed_model_pres))
## 20 75
## 2 21hist(resid(root_shoot_seed_model_pres))
shapiro.test(resid(root_shoot_seed_model_pres))##
## Shapiro-Wilk normality test
##
## data: resid(root_shoot_seed_model_pres)
## W = 0.97141, p-value = 0.6602#0.6602Raw Statistical output
anova(root_shoot_seed_model_pres, type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 5.2766 5.2766 1 22 9.7046 0.005042 **
## soil_root 1.1572 1.1572 1 22 2.1283 0.158731
## precip:soil_root 4.8231 4.8231 1 22 8.8704 0.006934 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(root_shoot_seed_model_pres, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B 2.08 0.175 5.22 1.64 2.52
## S.B 2.59 0.356 12.75 1.82 3.37
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - S.B -0.513 0.386 21.9 -1.331 0.1970
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.emmeans(root_shoot_seed_model_pres, pairwise~soil_root|precip)## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 2.06 0.244 12.8 1.529 2.59
## S.B 1.52 0.341 20.1 0.811 2.23
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 2.11 0.254 14.8 1.564 2.65
## S.B 3.67 0.633 15.7 2.324 5.01
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.535 0.425 20.9 1.258 0.2224
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B -1.562 0.681 20.7 -2.294 0.0324
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.Formatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 22 | 9.70 | 0.005 |
| 1 | 22 | 2.13 | 0.159 |
| 1 | 22 | 8.87 | 0.007 |
| ### Or | igin Root:sh | oot ratio g | iven germination |
Diagnostic Graphs
#now root:shoot ratio
fin_dataSG_biomass_seed_surv_orig_w_germ=subset(fin_dataSG_biomass_seed_surv_trt_given_germ, soil_status =="L")
root_shoot_seed_model_orig= lmer(log(root_shoot)~precip*soil_root+(1|block), data= fin_dataSG_biomass_seed_surv_orig_w_germ)
qqPlot(resid(root_shoot_seed_model_orig))
## 51 52
## 20 21hist(resid(root_shoot_seed_model_orig))
shapiro.test(resid(root_shoot_seed_model_orig))##
## Shapiro-Wilk normality test
##
## data: resid(root_shoot_seed_model_orig)
## W = 0.96601, p-value = 0.2958#0.2958Raw Statistical output
anova(root_shoot_seed_model_orig, type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 0.86205 0.86205 1 33.304 1.0003 0.3245
## soil_root 0.73422 0.73422 1 32.163 0.8519 0.3629
## precip:soil_root 0.41700 0.41700 1 31.393 0.4839 0.4918emmeans(root_shoot_seed_model_orig, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B 2.07 0.238 9.66 1.53 2.60
## L.R 2.35 0.250 10.07 1.79 2.91
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - L.R -0.285 0.314 31.8 -0.907 0.3711
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.emmeans(root_shoot_seed_model_orig, pairwise~soil_root|precip)## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 2.02 0.316 20.6 1.36 2.68
## L.R 2.09 0.289 17.5 1.48 2.70
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 2.11 0.330 23.0 1.43 2.80
## L.R 2.61 0.382 25.2 1.83 3.40
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - L.R -0.0709 0.402 31.0 -0.176 0.8612
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - L.R -0.4989 0.477 31.5 -1.046 0.3033
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.Formatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 33.30 | 1.00 | 0.324 |
| 1 | 32.16 | 0.85 | 0.363 |
| 1 | 31.39 | 0.48 | 0.492 |
Soil nitrate
Diagnostic Graphs
SG_inorg_N_trt$soil_root=with(SG_inorg_N_trt, interaction(soil_status,root_association))
SG_inorg_N_seed_trt= subset(SG_inorg_N_trt, life_stage=="S")
SG_soil_nit_model= lmer((ug_N_NO3_g_dry_soil)~precip*soil_root+(1|block), data= SG_inorg_N_seed_trt)## boundary (singular) fit: see ?isSingularqqPlot(resid(SG_soil_nit_model))
## [1] 1 2hist(resid(SG_soil_nit_model))
shapiro.test(resid(SG_soil_nit_model))##
## Shapiro-Wilk normality test
##
## data: resid(SG_soil_nit_model)
## W = 0.95913, p-value = 0.7402#0.7402Raw Statistical output
anova(SG_soil_nit_model, type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 11.4658 11.4658 1 7 38.967 0.0004273 ***
## soil_root 9.9131 4.9566 2 7 16.845 0.0021117 **
## precip:soil_root 8.8261 4.4131 2 7 14.998 0.0029466 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(SG_soil_nit_model, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B 5.98 0.344 7.00 5.17 6.80
## S.B 3.33 0.369 7.00 2.46 4.21
## L.R 4.68 0.257 5.49 4.04 5.32
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - S.B 2.65 0.505 5.35 5.249 0.0064
## L.B - L.R 1.30 0.419 4.74 3.111 0.0621
## S.B - L.R -1.35 0.450 5.49 -2.991 0.0603
##
## Results are averaged over the levels of: precip
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimatesemmeans(SG_soil_nit_model, pairwise~soil_root|precip)## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 3.57 0.344 7 2.76 4.38
## S.B 3.31 0.436 7 2.28 4.35
## L.R 3.91 0.271 7 3.27 4.55
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 8.40 0.690 7 6.77 10.03
## S.B 3.35 0.664 7 1.78 4.92
## L.R 5.45 0.436 7 4.42 6.48
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.254 0.571 6.37 0.445 0.8984
## L.B - L.R -0.339 0.438 4.82 -0.773 0.7346
## S.B - L.R -0.593 0.514 5.49 -1.153 0.5230
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 5.043 0.985 7.00 5.123 0.0034
## L.B - L.R 2.946 0.784 5.49 3.758 0.0254
## S.B - L.R -2.098 0.848 7.00 -2.475 0.0953
##
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimatesFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 29.56 | 4.28 | 0.047 |
| 1 | 28.58 | 8.82 | 0.006 |
| 1 | 30.33 | 1.85 | 0.183 |
| Graph | for publicat | ion | |
| <img s | rc="MERDS_R_ | code_SG_reA | nalysis_20200828_files/figure-html/unnamed-chunk-60-1.png" width=“672” /> |
Soil ammonium
Diagnostic Graphs
SG_soil_amm_model= lmer((ug_N_NH4_g_dry_soil_negto0)~precip*soil_root+(1|block), data= SG_inorg_N_seed_trt)## boundary (singular) fit: see ?isSingularqqPlot(resid(SG_soil_amm_model))
## [1] 12 13hist(resid(SG_soil_amm_model))
shapiro.test(resid(SG_soil_amm_model))##
## Shapiro-Wilk normality test
##
## data: resid(SG_soil_amm_model)
## W = 0.60938, p-value = 7.899e-05#p-value = 7.899e-05Raw Statistical output
anova(SG_soil_amm_model, type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 0.2271 0.22709 1 7 0.2559 0.6285
## soil_root 5.3807 2.69034 2 7 3.0317 0.1126
## precip:soil_root 0.0893 0.04463 2 7 0.0503 0.9513emmeans(SG_soil_amm_model, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.131 0.597 7.00 -1.281 1.54
## S.B 1.851 0.641 7.00 0.335 3.37
## L.R 0.279 0.446 5.49 -0.837 1.40
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - S.B -1.721 0.876 5.35 -1.964 0.2106
## L.B - L.R -0.148 0.728 4.74 -0.204 0.9774
## S.B - L.R 1.572 0.781 5.49 2.013 0.1966
##
## Results are averaged over the levels of: precip
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimatesemmeans(SG_soil_amm_model, pairwise~soil_root|precip)## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.0406 0.597 7 -1.3716 1.45
## S.B 1.7611 0.758 7 -0.0307 3.55
## L.R 0.0088 0.471 7 -1.1050 1.12
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.2210 1.198 7 -2.6121 3.05
## S.B 1.9418 1.154 7 -0.7864 4.67
## L.R 0.5498 0.758 7 -1.2420 2.34
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B -1.7205 0.992 6.37 -1.735 0.2644
## L.B - L.R 0.0318 0.761 4.82 0.042 0.9990
## S.B - L.R 1.7523 0.892 5.49 1.964 0.2085
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B -1.7207 1.710 7.00 -1.006 0.5963
## L.B - L.R -0.3288 1.361 5.49 -0.242 0.9685
## S.B - L.R 1.3919 1.472 7.00 0.946 0.6310
##
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimatesGraph for publication
## Warning: Removed 2 rows containing missing values (geom_text).
TRANSPLANT PORTION OF THE EXPERIEMENT BEGINING
Was there treatment effects on intial survival
Graph 
Was there a bias in initial height?
Diagnostic Graphs
SG_intial_height_model= lmer((int_height_cm)^(1/3)~precip*soil_root+(1|block), data= fin_dataSG_trans_surv_trt_planting)
qqPlot(resid(SG_intial_height_model))
## [1] 14 7hist(resid(SG_intial_height_model))
shapiro.test(resid(SG_intial_height_model))##
## Shapiro-Wilk normality test
##
## data: resid(SG_intial_height_model)
## W = 0.93125, p-value = 0.0001383#p-value = 0.0001383Raw Statistical output
anova(SG_intial_height_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 0.000081 0.0000813 1 80 0.0033 0.9546
## soil_root 0.044662 0.0223308 2 80 0.8961 0.4122
## precip:soil_root 0.060344 0.0301722 2 80 1.2108 0.3034Formatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 80 | 0.00 | 0.955 |
| 2 | 80 | 0.90 | 0.412 |
| 2 | 80 | 1.21 | 0.303 |
| ### To | tal biomass |
Diagnostic Graphs
trans_biomas_model= lmer(log(total_biomass_0+.001)~precip*soil_root+(1|block), data= data_SG_biomass_trans_surv_trt)
qqPlot(resid(trans_biomas_model))
## [1] 29 41hist(resid(trans_biomas_model))
shapiro.test(resid(trans_biomas_model))##
## Shapiro-Wilk normality test
##
## data: resid(trans_biomas_model)
## W = 0.96619, p-value = 0.01918Raw Statistical output
anova(trans_biomas_model, type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 31.948 31.948 1 80 13.4201 0.0004457 ***
## soil_root 23.301 11.651 2 80 4.8939 0.0098833 **
## precip:soil_root 15.880 7.940 2 80 3.3353 0.0406192 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(trans_biomas_model, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B -3.39 0.323 15.6 -4.08 -2.71
## S.B -2.48 0.323 15.6 -3.17 -1.80
## L.R -3.67 0.323 15.6 -4.36 -2.99
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log(mu + 0.001) (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - S.B -0.909 0.398 80 -2.282 0.0641
## L.B - L.R 0.284 0.398 80 0.712 0.7570
## S.B - L.R 1.193 0.398 80 2.994 0.0101
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesemmeans(trans_biomas_model, pairwise~soil_root|precip)## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B -3.16 0.429 38.4 -4.03 -2.294
## S.B -1.30 0.429 38.4 -2.17 -0.431
## L.R -3.30 0.429 38.4 -4.17 -2.434
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B -3.62 0.429 38.4 -4.49 -2.754
## S.B -3.67 0.429 38.4 -4.53 -2.799
## L.R -4.05 0.429 38.4 -4.92 -3.181
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log(mu + 0.001) (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B -1.8632 0.563 80 -3.307 0.0040
## L.B - L.R 0.1403 0.563 80 0.249 0.9664
## S.B - L.R 2.0036 0.563 80 3.556 0.0018
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.0449 0.563 80 0.080 0.9965
## L.B - L.R 0.4272 0.563 80 0.758 0.7295
## S.B - L.R 0.3823 0.563 80 0.678 0.7767
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 80 | 13.42 | 0.000 |
| 2 | 80 | 4.89 | 0.010 |
| 2 | 80 | 3.34 | 0.041 |
Graph for publication
Presence at total biomass
Diagnostic Graphs
data_SG_biomass_trans_surv_pres=subset(data_SG_biomass_trans_surv_trt, root_association =="B")
trans_biomas_model_pres= lmer((total_biomass_0+.001)^(1/4)~precip*soil_root+(1|block), data= data_SG_biomass_trans_surv_pres)## boundary (singular) fit: see ?isSingularqqPlot(resid(trans_biomas_model_pres))
## 85 34
## 55 19hist(resid(trans_biomas_model_pres))
shapiro.test(resid(trans_biomas_model_pres))##
## Shapiro-Wilk normality test
##
## data: resid(trans_biomas_model_pres)
## W = 0.97499, p-value = 0.254#p-value = 0.254Raw Statistical output
anova(trans_biomas_model_pres, type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 0.38241 0.38241 1 56 13.2458 0.0005969 ***
## soil_root 0.29851 0.29851 1 56 10.3396 0.0021637 **
## precip:soil_root 0.25587 0.25587 1 56 8.8628 0.0042933 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(trans_biomas_model_pres, pairwise~soil_root)## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect## NOTE: Results may be misleading due to involvement in interactions## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.455 0.031 14.9 0.389 0.521
## S.B 0.596 0.031 14.9 0.530 0.662
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - S.B -0.141 0.0439 52 -3.216 0.0022
##
## Results are averaged over the levels of: precip
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-rogeremmeans(trans_biomas_model_pres, pairwise~soil_root|precip)## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.469 0.0439 37.8 0.380 0.558
## S.B 0.741 0.0439 37.8 0.652 0.830
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.440 0.0439 37.8 0.351 0.529
## S.B 0.451 0.0439 37.8 0.362 0.540
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B -0.2717 0.062 52 -4.379 0.0001
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B -0.0105 0.062 52 -0.169 0.8667
##
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-rogerFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 56 | 13.25 | 0.001 |
| 1 | 56 | 10.34 | 0.002 |
| 1 | 56 | 8.86 | 0.004 |
Origin at total biomass
Diagnostic Graphs
data_SG_biomass_trans_surv_orig=subset(data_SG_biomass_trans_surv_trt, soil_status =="L")
nrow(data_SG_biomass_trans_surv_orig)## [1] 60trans_biomas_model_orig= lmer((total_biomass_0+0.001)^(1/4)~precip*soil_root+(1|block), data= data_SG_biomass_trans_surv_orig)
qqPlot(resid(trans_biomas_model_orig))
## [1] 2 29hist(resid(trans_biomas_model_orig))
shapiro.test(resid(trans_biomas_model_orig))##
## Shapiro-Wilk normality test
##
## data: resid(trans_biomas_model_orig)
## W = 0.97214, p-value = 0.1856#p-value = 0.1856Raw Statistical output
anova(trans_biomas_model_orig)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 0.0212028 0.0212028 1 52 1.0133 0.3188
## soil_root 0.0111201 0.0111201 1 52 0.5314 0.4693
## precip:soil_root 0.0010927 0.0010927 1 52 0.0522 0.8201emmeans(trans_biomas_model_orig, pairwise~soil_root)## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect## NOTE: Results may be misleading due to involvement in interactions## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.455 0.0313 9.4 0.384 0.525
## L.R 0.428 0.0313 9.4 0.357 0.498
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - L.R 0.0272 0.0373 52 0.729 0.4693
##
## Results are averaged over the levels of: precip
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-rogeremmeans(trans_biomas_model_orig, pairwise~soil_root|precip)## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.469 0.041 23.3 0.385 0.554
## L.R 0.451 0.041 23.3 0.366 0.535
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.440 0.041 23.3 0.356 0.525
## L.R 0.404 0.041 23.3 0.320 0.489
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - L.R 0.0187 0.0528 52 0.354 0.7249
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - L.R 0.0358 0.0528 52 0.677 0.5014
##
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-rogerFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 52 | 1.01 | 0.319 |
| 1 | 52 | 0.53 | 0.469 |
| 1 | 52 | 0.05 | 0.820 |
Root:shoot ratio
Diagnostic Graphs
root_shoot_model_na= lmer(log(root_shoot)~precip*soil_root+(1|block), data= data_SG_biomass_trans_surv_trt_na)## boundary (singular) fit: see ?isSingularqqPlot(resid(root_shoot_model_na))
## 53 45
## 43 35hist(resid(root_shoot_model_na))
shapiro.test(resid(root_shoot_model_na))##
## Shapiro-Wilk normality test
##
## data: resid(root_shoot_model_na)
## W = 0.94233, p-value = 0.001287#p-value = 0.001287
plot(root_shoot_model_na)
Raw Statistical output
anova(root_shoot_model_na)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 2.3613 2.36129 1 74 1.4419 0.2337
## soil_root 1.0009 0.50045 2 74 0.3056 0.7376
## precip:soil_root 1.5907 0.79534 2 74 0.4857 0.6172emmeans(root_shoot_model_na, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B 1.13 0.243 26.7 0.628 1.62
## S.B 1.03 0.255 29.7 0.511 1.55
## L.R 1.31 0.257 28.1 0.782 1.83
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.0942 0.352 70.7 0.267 0.9614
## L.B - L.R -0.1818 0.354 71.9 -0.514 0.8648
## S.B - L.R -0.2759 0.361 71.3 -0.765 0.7258
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesemmeans(root_shoot_model_na, pairwise~soil_root|precip)## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.814 0.330 54.4 0.152 1.48
## S.B 1.054 0.330 54.4 0.392 1.72
## L.R 1.077 0.330 54.4 0.415 1.74
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 1.438 0.356 58.5 0.726 2.15
## S.B 1.010 0.389 61.7 0.232 1.79
## L.R 1.539 0.393 56.9 0.752 2.33
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B -0.2403 0.467 70.0 -0.514 0.8647
## L.B - L.R -0.2630 0.467 70.0 -0.563 0.8402
## S.B - L.R -0.0227 0.467 70.0 -0.049 0.9987
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.4286 0.527 71.2 0.814 0.6959
## L.B - L.R -0.1006 0.531 73.0 -0.189 0.9804
## S.B - L.R -0.5292 0.550 72.1 -0.962 0.6031
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 74 | 1.44 | 0.234 |
| 2 | 74 | 0.31 | 0.738 |
| 2 | 74 | 0.49 | 0.617 |
Graph for publication
Presence Root:shoot ratio
Diagnostic Graphs
data_SG_biomass_trans_surv_pres_na=subset(data_SG_biomass_trans_surv_trt_na, root_association =="B")
root_shoot_model_pres_na= lmer((root_shoot)^(1/4)~precip*soil_root+(1|block), data= data_SG_biomass_trans_surv_pres_na)## boundary (singular) fit: see ?isSingularqqPlot(resid(root_shoot_model_pres_na))
## 25 45
## 9 24hist(resid(root_shoot_model_pres_na))
shapiro.test(resid(root_shoot_model_pres_na))##
## Shapiro-Wilk normality test
##
## data: resid(root_shoot_model_pres_na)
## W = 0.94371, p-value = 0.01335#p-value = 0.01335
plot(root_shoot_model_pres_na)
Raw Statistical output
anova(root_shoot_model_pres_na)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 0.175840 0.175840 1 50 1.1108 0.2970
## soil_root 0.009263 0.009263 1 50 0.0585 0.8099
## precip:soil_root 0.250414 0.250414 1 50 1.5818 0.2143emmeans(root_shoot_model_pres_na, pairwise~soil_root)## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect## NOTE: Results may be misleading due to involvement in interactions## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B 1.38 0.0756 13.6 1.22 1.55
## S.B 1.36 0.0795 15.4 1.19 1.53
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.0264 0.11 46.7 0.241 0.8107
##
## Results are averaged over the levels of: precip
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-rogeremmeans(root_shoot_model_pres_na, pairwise~soil_root|precip)## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 1.26 0.103 32.5 1.05 1.47
## S.B 1.37 0.103 32.5 1.16 1.58
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 1.51 0.111 35.8 1.28 1.73
## S.B 1.35 0.121 38.5 1.10 1.59
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B -0.111 0.145 46.0 -0.763 0.4492
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.164 0.164 47.2 0.997 0.3238
##
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-rogerFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 50 | 1.11 | 0.297 |
| 1 | 50 | 0.06 | 0.810 |
| 1 | 50 | 1.58 | 0.214 |
Origin Root:shoot ratio
Diagnostic Graphs
data_SG_biomass_trans_surv_orig_na=subset(data_SG_biomass_trans_surv_trt_na,soil_status =="L")
root_shoot_model_orig_na= lmer(log(root_shoot)~precip*soil_root+(1|block), data= data_SG_biomass_trans_surv_orig_na)## boundary (singular) fit: see ?isSingularqqPlot(resid(root_shoot_model_orig_na))
## 53 25
## 32 20hist(resid(root_shoot_model_orig_na))
shapiro.test(resid(root_shoot_model_orig_na))##
## Shapiro-Wilk normality test
##
## data: resid(root_shoot_model_orig_na)
## W = 0.94863, p-value = 0.02169#p-value = 0.02169
plot(root_shoot_model_orig_na)
Raw Statistical output
anova(root_shoot_model_orig_na)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 3.9134 3.9134 1 50 2.4860 0.1212
## soil_root 0.4389 0.4389 1 50 0.2788 0.5998
## precip:soil_root 0.0875 0.0875 1 50 0.0556 0.8145emmeans(root_shoot_model_orig_na, pairwise~soil_root|precip)## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.814 0.324 32.3 0.155 1.47
## L.R 1.077 0.324 32.3 0.417 1.74
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 1.438 0.350 35.6 0.729 2.15
## L.R 1.539 0.388 34.2 0.750 2.33
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - L.R -0.263 0.458 46 -0.574 0.5688
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - L.R -0.101 0.523 49 -0.192 0.8484
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.Formatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 50 | 2.49 | 0.121 |
| 1 | 50 | 0.28 | 0.600 |
| 1 | 50 | 0.06 | 0.815 |
Final survival
data_SG_biomass_trans_surv_trt$surv=data_SG_biomass_trans_surv_trt$total_biomass
data_SG_biomass_trans_surv_trt$surv[is.na(data_SG_biomass_trans_surv_trt$surv)]=0
data_SG_biomass_trans_surv_trt$surv[data_SG_biomass_trans_surv_trt$surv!=0]=1Diagnostic Graphs
Raw Statistical output
Graph for publication
Survival of the transplant
Final survival in the drought treatment only since all pots had live transs in the ambient treatments
Diagnostic Graphs
data_SG_biomass_trans_surv_drought=subset(data_SG_biomass_trans_surv_trt, precip=="D")
drought_surv_model= glmer(surv~soil_root+(1|block), data= data_SG_biomass_trans_surv_drought, family = binomial)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0139762 (tol = 0.002, component 1)qqPlot(resid(drought_surv_model))
## [1] 29 41hist(resid(drought_surv_model))
Raw Statistical output
Anova(drought_surv_model,type = 3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: surv
## Chisq Df Pr(>Chisq)
## (Intercept) 88736 1 < 2.2e-16 ***
## soil_root 15916 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(drought_surv_model, pairwise~soil_root)## $emmeans
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B 2.15 0.00716 Inf 2.133 2.16
## S.B 1.19 0.44169 Inf 0.323 2.05
## L.R 1.19 0.44169 Inf 0.323 2.05
##
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## L.B - S.B 9.58e-01 0.442 Inf 2.168 0.0767
## L.B - L.R 9.58e-01 0.442 Inf 2.168 0.0767
## S.B - L.R 2.16e-05 0.883 Inf 0.000 1.0000
##
## Results are given on the log odds ratio (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesFormatted Anova table
| Df | Chi.Sq | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 88735.85 | 0 |
| soil_root | 2 | 15916.28 | 0 |
Presence survival of the transplant
Diagnostic Graphs
data_SG_biomass_trans_surv_drought_pres=subset(data_SG_biomass_trans_surv_drought, root_association =="B")
drought_surv_model_pres= glmer(surv~soil_root+(1|block), data= data_SG_biomass_trans_surv_drought_pres, family = binomial)
qqPlot(resid(drought_surv_model_pres))
## 29 16
## 14 1hist(resid(drought_surv_model_pres))
Raw Statistical output
Anova(drought_surv_model_pres,type = 3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: surv
## Chisq Df Pr(>Chisq)
## (Intercept) 7.2643 1 0.007034 **
## soil_root 0.8025 1 0.370340
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(drought_surv_model_pres, pairwise~soil_root)## $emmeans
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B 1.89 0.815 Inf 0.288 3.48
## S.B 1.02 0.619 Inf -0.192 2.23
##
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## L.B - S.B 0.865 0.966 Inf 0.896 0.3703
##
## Results are given on the log odds ratio (not the response) scale.Formatted Anova table
| Df | Chi.Sq | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 7.26 | 0.007 |
| soil_root | 1 | 0.80 | 0.370 |
Origin survival of the transplant
Diagnostic Graphs
data_SG_biomass_trans_surv_drought_orig=subset(data_SG_biomass_trans_surv_drought, soil_status =="L")
drought_surv_model_orig= glmer(surv~soil_root+(1|block), data= data_SG_biomass_trans_surv_drought_orig, family = binomial)
qqPlot(resid(drought_surv_model_orig))
## [1] 29 16hist(resid(drought_surv_model_orig))
Raw Statistical output
Anova(drought_surv_model_orig,type = 3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: surv
## Chisq Df Pr(>Chisq)
## (Intercept) 4.9500 1 0.02609 *
## soil_root 0.8716 1 0.35052
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(drought_surv_model_orig, pairwise~soil_root)## $emmeans
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B 2.12 0.996 Inf 0.173 4.08
## L.R 1.17 0.791 Inf -0.377 2.73
##
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## L.B - L.R 0.95 1.02 Inf 0.934 0.3505
##
## Results are given on the log odds ratio (not the response) scale.Formatted Anova table
| Df | Chi.Sq | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 4.95 | 0.026 |
| soil_root | 1 | 0.87 | 0.351 |
Root traits
Diagnostic Graphs
SRL_trans_model= lmer((SRL_length_drymass)~precip*soil_root+(1|block), data= SG_roottraits_trt)
qqPlot(resid(SRL_trans_model))
## [1] 18 8hist(resid(SRL_trans_model))
shapiro.test(resid(SRL_trans_model))##
## Shapiro-Wilk normality test
##
## data: resid(SRL_trans_model)
## W = 0.97902, p-value = 0.8396Raw Statistical output
anova(SRL_trans_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 119509598 119509598 1 18.017 5.5697 0.02975 *
## soil_root 33012605 16506303 2 17.454 0.7693 0.47843
## precip:soil_root 174500103 87250052 2 17.454 4.0663 0.03551 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(SRL_trans_model, pairwise~soil_root|precip)## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 14406 2145 20.6 9939 18873
## S.B 10446 2145 20.6 5979 14913
## L.R 11348 2145 20.6 6882 15815
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 12476 2145 20.6 8009 16942
## S.B 21317 2844 21.0 15402 27232
## L.R 15338 2430 20.8 10282 20394
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 3960 2930 17.1 1.352 0.3872
## L.B - L.R 3058 2930 17.1 1.044 0.5604
## S.B - L.R -902 2930 17.1 -0.308 0.9492
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B -8841 3474 18.2 -2.545 0.0504
## L.B - L.R -2862 3144 17.6 -0.910 0.6411
## S.B - L.R 5979 3593 17.7 1.664 0.2462
##
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimatesFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 18.02 | 5.57 | 0.030 |
| 2 | 17.45 | 0.77 | 0.478 |
| 2 | 17.45 | 4.07 | 0.036 |
| Graph | for publicat | ion |
Presence specific root length
Diagnostic Graphs
SG_roottraits_pres=subset(SG_roottraits_trt, root_association =="B")
SRL_trans_model_pres= lmer((SRL_length_drymass)~precip*soil_root+(1|block), data= SG_roottraits_pres)
qqPlot(resid(SRL_trans_model_pres))
## 18 8
## 9 4hist(resid(SRL_trans_model_pres))
shapiro.test(resid(SRL_trans_model_pres))##
## Shapiro-Wilk normality test
##
## data: resid(SRL_trans_model_pres)
## W = 0.97446, p-value = 0.876Raw Statistical output
anova(SRL_trans_model_pres)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 95886515 95886515 1 10.131 4.8407 0.05209 .
## soil_root 31805805 31805805 1 10.131 1.6057 0.23345
## precip:soil_root 188806702 188806702 1 10.131 9.5317 0.01133 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(SRL_trans_model_pres, pairwise~soil_root|precip)## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 14406 2304 12.0 9387 19424
## S.B 10446 2304 12.0 5428 15464
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 12476 2304 12.0 7457 17494
## S.B 21949 2987 13.8 15532 28366
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 3960 2815 10.0 1.407 0.1897
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B -9473 3397 10.8 -2.789 0.0180
##
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-rogerFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 10.13 | 4.84 | 0.052 |
| 1 | 10.13 | 1.61 | 0.233 |
| 1 | 10.13 | 9.53 | 0.011 |
Origin specific root length
Diagnostic Graphs
SG_roottraits_orig=subset(SG_roottraits_trt,soil_status =="L")
SRL_trans_model_orig= lmer((SRL_length_drymass)~precip*soil_root+(1|block), data= SG_roottraits_orig)
qqPlot(resid(SRL_trans_model_orig))
## 18 8
## 15 8hist(resid(SRL_trans_model_orig))
shapiro.test(resid(SRL_trans_model_orig))##
## Shapiro-Wilk normality test
##
## data: resid(SRL_trans_model_orig)
## W = 0.968, p-value = 0.7359Raw Statistical output
anova(SRL_trans_model_orig)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 4777217 4777217 1 11.588 0.1724 0.6856
## soil_root 66735 66735 1 11.588 0.0024 0.9617
## precip:soil_root 40567718 40567718 1 11.588 1.4637 0.2504emmeans(SRL_trans_model_orig, pairwise~soil_root|precip)## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 14406 2390 15 9312 19500
## L.R 11348 2390 15 6254 16443
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 12476 2390 15 7381 17570
## L.R 15295 2741 15 9452 21138
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - L.R 3058 3330 11.0 0.918 0.3781
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - L.R -2819 3590 11.6 -0.785 0.4480
##
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-rogerFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 11.59 | 0.17 | 0.686 |
| 1 | 11.59 | 0.00 | 0.962 |
| 1 | 11.59 | 1.46 | 0.250 |
Root length density
Diagnostic Graphs
RLD_trans_model= lmer((RLD_length_volume)~precip*soil_root+(1|block), data= SG_roottraits_trt)
qqPlot(resid(RLD_trans_model))
## [1] 23 1hist(resid(RLD_trans_model))
Raw Statistical output
anova(RLD_trans_model, type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 0.4102 0.4102 1 16.865 5.0491 0.038322 *
## soil_root 6.3528 3.1764 2 16.061 39.0983 6.734e-07 ***
## precip:soil_root 1.2403 0.6201 2 16.061 7.6332 0.004677 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(RLD_trans_model, pairwise~soil_root|precip)## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.548 0.130 20.9 0.277 0.818
## S.B 2.071 0.130 20.9 1.801 2.342
## L.R 0.487 0.130 20.9 0.216 0.757
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.472 0.130 20.9 0.202 0.743
## S.B 1.195 0.173 21.0 0.835 1.555
## L.R 0.683 0.148 20.9 0.376 0.990
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B -1.5238 0.180 17.1 -8.453 <.0001
## L.B - L.R 0.0609 0.180 17.1 0.338 0.9393
## S.B - L.R 1.5847 0.180 17.1 8.791 <.0001
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B -0.7229 0.214 18.4 -3.384 0.0086
## L.B - L.R -0.2109 0.193 17.7 -1.091 0.5318
## S.B - L.R 0.5119 0.221 17.8 2.315 0.0794
##
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimatesFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 16.86 | 5.05 | 0.038 |
| 2 | 16.06 | 39.10 | 0.000 |
| 2 | 16.06 | 7.63 | 0.005 |
| <img s | rc="MERDS_R_ | code_SG_reA | nalysis_20200828_files/figure-html/unnamed-chunk-119-1.png" width=“672” /> |
Rhizosheath
Diagnostic Graphs
rhizosheath_model= lmer(log(RhizosheathSoil_DryRoots_g +1)~precip*soil_root+(1|block), data= SG_roottraits_trt)## boundary (singular) fit: see ?isSingularqqPlot(resid(rhizosheath_model))
## [1] 16 2hist(resid(rhizosheath_model))
shapiro.test(resid(rhizosheath_model))##
## Shapiro-Wilk normality test
##
## data: resid(rhizosheath_model)
## W = 0.97019, p-value = 0.6068#0.6068Raw Statistical output
anova(rhizosheath_model, type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 0.1008 0.10081 1 21 0.1016 0.75304
## soil_root 5.1477 2.57383 2 21 2.5945 0.09841 .
## precip:soil_root 0.1843 0.09213 2 21 0.0929 0.91169
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Formatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 21 | 0.10 | 0.753 |
| 2 | 21 | 2.59 | 0.098 |
| 2 | 21 | 0.09 | 0.912 |
Graph for publication 
Presence Rhizosheath
Diagnostic Graphs
SG_roottraits_pres=subset(SG_roottraits_trt, root_association =="B")
rhizosheath_model_pres= lmer(log(RhizosheathSoil_DryRoots_g)~precip*soil_root+(1|block), data= SG_roottraits_pres)
qqPlot(resid(rhizosheath_model_pres))
## 19 24
## 10 15hist(resid(rhizosheath_model_pres))
shapiro.test(resid(rhizosheath_model_pres))##
## Shapiro-Wilk normality test
##
## data: resid(rhizosheath_model_pres)
## W = 0.95982, p-value = 0.5982#0.5982Raw Statistical output
anova(rhizosheath_model_pres, type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 0.59058 0.59058 1 10.064 1.4693 0.2531
## soil_root 1.72540 1.72540 1 10.064 4.2926 0.0649 .
## precip:soil_root 0.03550 0.03550 1 10.064 0.0883 0.7724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Formatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 10.06 | 1.47 | 0.253 |
| 1 | 10.06 | 4.29 | 0.065 |
| 1 | 10.06 | 0.09 | 0.772 |
| ### Or | igin Rhizosh | eath |
Diagnostic Graphs
SG_roottraits_orig=subset(SG_roottraits_trt,soil_status =="L")
rhizosheath_model_orig= lmer(log(RhizosheathSoil_DryRoots_g+1)~precip*soil_root+(1|block), data= SG_roottraits_orig)## boundary (singular) fit: see ?isSingularqqPlot(resid(rhizosheath_model_orig))
## 16 2
## 13 2hist(resid(rhizosheath_model_orig))
shapiro.test(resid(rhizosheath_model_orig))##
## Shapiro-Wilk normality test
##
## data: resid(rhizosheath_model_orig)
## W = 0.96907, p-value = 0.7577#0.7577Raw Statistical output
anova(rhizosheath_model_orig, type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 0.02583 0.02583 1 15 0.0212 0.8862
## soil_root 1.90925 1.90925 1 15 1.5649 0.2301
## precip:soil_root 0.15779 0.15779 1 15 0.1293 0.7241Formatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 15 | 0.02 | 0.886 |
| 1 | 15 | 1.56 | 0.230 |
| 1 | 15 | 0.13 | 0.724 |
Soil nitrogen
Diagnostic Graphs
SG_soil_nit_trans_model= lmer((ug_N_NO3_g_dry_soil)~precip*soil_root+(1|block), data= SG_inorg_N_trans_trt)
qqPlot(resid(SG_soil_nit_trans_model))
## 17 15
## 4 2hist(resid(SG_soil_nit_trans_model))
shapiro.test(resid(SG_soil_nit_trans_model))##
## Shapiro-Wilk normality test
##
## data: resid(SG_soil_nit_trans_model)
## W = 0.96397, p-value = 0.4755Raw Statistical output
anova(SG_soil_nit_trans_model, type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 5.3802 5.3802 1 16.868 15.2231 0.001162 **
## soil_root 6.4455 3.2227 2 17.249 9.1186 0.001986 **
## precip:soil_root 3.2720 1.6360 2 16.841 4.6289 0.025001 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(SG_soil_nit_trans_model, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B 4.42 0.230 14.5 3.93 4.92
## S.B 3.44 0.201 13.6 3.01 3.87
## L.R 4.52 0.234 16.0 4.03 5.02
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.9848 0.289 17.4 3.413 0.0086
## L.B - L.R -0.0989 0.307 16.8 -0.323 0.9445
## S.B - L.R -1.0837 0.292 16.9 -3.715 0.0047
##
## Results are averaged over the levels of: precip
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimatesemmeans(SG_soil_nit_trans_model, pairwise~soil_root|precip)## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 3.86 0.312 19.8 3.21 4.51
## S.B 3.45 0.275 19.6 2.87 4.02
## L.R 3.69 0.275 19.6 3.11 4.26
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 4.99 0.312 19.8 4.34 5.64
## S.B 3.43 0.275 19.6 2.86 4.01
## L.R 5.36 0.365 20.0 4.59 6.12
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.409 0.403 16.8 1.015 0.5780
## L.B - L.R 0.168 0.403 16.8 0.415 0.9099
## S.B - L.R -0.242 0.376 16.1 -0.643 0.7986
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 1.560 0.403 16.8 3.867 0.0034
## L.B - L.R -0.365 0.462 16.8 -0.791 0.7133
## S.B - L.R -1.925 0.446 17.4 -4.315 0.0012
##
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimatesFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 16.87 | 15.22 | 0.001 |
| 2 | 17.25 | 9.12 | 0.002 |
| 2 | 16.84 | 4.63 | 0.025 |
| Graph | for publicat | ion | |
| <img s | rc="MERDS_R_ | code_SG_reA | nalysis_20200828_files/figure-html/unnamed-chunk-134-1.png" width=“672” /> |
Presence nitrate transplant
Diagnostic Graphs
SG_inorg_N_trans_pres=subset(SG_inorg_N_trans_trt, root_association =="B")
SG_soil_nit_trans_model_pres= lmer((ug_N_NO3_g_dry_soil)~precip*soil_root+(1|block), data= SG_inorg_N_trans_pres)
qqPlot(resid(SG_soil_nit_trans_model_pres))
## 17 19
## 1 3hist(resid(SG_soil_nit_trans_model_pres))
shapiro.test(resid(SG_soil_nit_trans_model_pres))##
## Shapiro-Wilk normality test
##
## data: resid(SG_soil_nit_trans_model_pres)
## W = 0.89259, p-value = 0.04276Raw Statistical output
anova(SG_soil_nit_trans_model_pres, type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 1.3872 1.3872 1 13.998 5.5927 0.033015 *
## soil_root 4.2187 4.2187 1 13.998 17.0078 0.001033 **
## precip:soil_root 1.4715 1.4715 1 13.998 5.9324 0.028831 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(SG_soil_nit_trans_model_pres, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B 4.41 0.183 10.2 4.01 4.82
## S.B 3.44 0.157 10.2 3.09 3.79
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.974 0.242 11.4 4.031 0.0018
##
## Results are averaged over the levels of: precip
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-rogeremmeans(SG_soil_nit_trans_model_pres, pairwise~soil_root|precip)## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 3.85 0.254 14 3.30 4.39
## S.B 3.45 0.223 14 2.97 3.93
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 4.98 0.254 14 4.44 5.53
## S.B 3.43 0.223 14 2.95 3.91
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 0.399 0.338 10.8 1.180 0.2632
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 1.550 0.338 10.8 4.585 0.0008
##
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-rogerFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 14 | 5.59 | 0.033 |
| 1 | 14 | 17.01 | 0.001 |
| 1 | 14 | 5.93 | 0.029 |
Origin nitrate transplant
Diagnostic Graphs
SG_inorg_N_trans_orig=subset(SG_inorg_N_trans_trt,soil_status =="L")
SG_soil_nit_trans_model_orig= lmer((ug_N_NO3_g_dry_soil)~precip*soil_root+(1|block), data= SG_inorg_N_trans_orig)
qqPlot(resid(SG_soil_nit_trans_model_orig))
## 17 19
## 4 6hist(resid(SG_soil_nit_trans_model_orig))
shapiro.test(resid(SG_soil_nit_trans_model_orig))##
## Shapiro-Wilk normality test
##
## data: resid(SG_soil_nit_trans_model_orig)
## W = 0.96308, p-value = 0.7179Raw Statistical output
anova(SG_soil_nit_trans_model_orig, type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 7.5837 7.5837 1 9.2345 14.0014 0.004408 **
## soil_root 0.0385 0.0385 1 9.2345 0.0711 0.795559
## precip:soil_root 0.2890 0.2890 1 9.2345 0.5335 0.483251
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(SG_soil_nit_trans_model_orig, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B 4.43 0.302 7.39 3.72 5.14
## L.R 4.53 0.307 8.59 3.83 5.23
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - L.R -0.1 0.383 8.99 -0.262 0.7992
##
## Results are averaged over the levels of: precip
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-rogeremmeans(SG_soil_nit_trans_model_orig, pairwise~soil_root|precip)## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 3.86 0.399 11.7 2.99 4.74
## L.R 3.69 0.351 11.6 2.92 4.46
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 5.00 0.399 11.7 4.13 5.87
## L.R 5.37 0.473 11.9 4.34 6.40
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - L.R 0.175 0.503 9.05 0.347 0.7362
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - L.R -0.376 0.579 8.95 -0.649 0.5328
##
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-rogerFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 9.23 | 14.00 | 0.004 |
| 1 | 9.23 | 0.07 | 0.796 |
| 1 | 9.23 | 0.53 | 0.483 |
Amonnium
Diagnostic Graphs
SG_soil_amm_trans_model= lmer(log(ug_N_NH4_g_dry_soil_negto0+0.007)~precip*soil_root+(1|block), data= SG_inorg_N_trans_trt)## boundary (singular) fit: see ?isSingularqqPlot(resid(SG_soil_amm_trans_model))
## 20 28
## 7 15hist(resid(SG_soil_amm_trans_model))
shapiro.test(resid(SG_soil_amm_trans_model))##
## Shapiro-Wilk normality test
##
## data: resid(SG_soil_amm_trans_model)
## W = 0.96033, p-value = 0.3981Raw Statistical output
anova(SG_soil_amm_trans_model, type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 7.010 7.0097 1 20 5.3624 0.03131 *
## soil_root 43.257 21.6285 2 20 16.5458 5.755e-05 ***
## precip:soil_root 8.962 4.4812 2 20 3.4281 0.05246 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(SG_soil_amm_trans_model, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B -2.8384 0.415 16.2 -3.718 -1.959
## S.B 0.0188 0.362 16.2 -0.747 0.784
## L.R -2.3990 0.428 17.7 -3.299 -1.499
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log(mu + 0.007) (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - S.B -2.857 0.551 17.5 -5.188 0.0002
## L.B - L.R -0.439 0.588 17.0 -0.747 0.7396
## S.B - L.R 2.418 0.560 17.1 4.317 0.0013
##
## Results are averaged over the levels of: precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesemmeans(SG_soil_amm_trans_model, pairwise~soil_root|precip)## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B -2.56 0.580 20 -3.7734 -1.3554
## S.B -1.10 0.511 20 -2.1707 -0.0376
## L.R -3.13 0.511 20 -4.2015 -2.0683
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B -3.11 0.580 20 -4.3214 -1.9034
## S.B 1.14 0.511 20 0.0751 2.2083
## L.R -1.66 0.686 20 -3.0934 -0.2327
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log(mu + 0.007) (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B -1.460 0.773 16.9 -1.889 0.1723
## L.B - L.R 0.571 0.773 16.9 0.738 0.7447
## S.B - L.R 2.031 0.723 16.2 2.808 0.0317
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B -4.254 0.773 16.9 -5.504 0.0001
## L.B - L.R -1.449 0.888 17.1 -1.633 0.2593
## S.B - L.R 2.805 0.855 17.7 3.279 0.0112
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 20 | 5.36 | 0.031 |
| 2 | 20 | 16.55 | 0.000 |
| 2 | 20 | 3.43 | 0.052 |
Graph for publication 
Presence Amonnium
Diagnostic Graphs
SG_inorg_N_trans_pres=subset(SG_inorg_N_trans_trt, root_association =="B")
SG_soil_amm_trans_model_pres= lmer(sqrt(ug_N_NH4_g_dry_soil_negto0)~precip*soil_root+(1|block), data= SG_inorg_N_trans_pres)## boundary (singular) fit: see ?isSingularqqPlot(resid(SG_soil_amm_trans_model_pres))
## 24 22
## 8 6hist(resid(SG_soil_amm_trans_model_pres))
shapiro.test(resid(SG_soil_amm_trans_model_pres))##
## Shapiro-Wilk normality test
##
## data: resid(SG_soil_amm_trans_model_pres)
## W = 0.93074, p-value = 0.2001Raw Statistical output
anova(SG_soil_amm_trans_model_pres, type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 1.3408 1.3408 1 14 18.813 0.0006824 ***
## soil_root 3.8066 3.8066 1 14 53.410 3.86e-06 ***
## precip:soil_root 1.7746 1.7746 1 14 24.899 0.0001982 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(SG_soil_amm_trans_model_pres, pairwise~soil_root|precip)## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.317 0.136 14 0.0245 0.609
## S.B 0.610 0.119 14 0.3543 0.866
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.234 0.136 14 -0.0581 0.526
## S.B 1.792 0.119 14 1.5354 2.048
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the sqrt (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B -0.294 0.181 10.8 -1.620 0.1338
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B -1.557 0.181 10.8 -8.596 <.0001
##
## Note: contrasts are still on the sqrt scale
## Degrees-of-freedom method: kenward-rogerFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 14 | 18.81 | 0.001 |
| 1 | 14 | 53.41 | 0.000 |
| 1 | 14 | 24.90 | 0.000 |
Origin ammonium transplant
Diagnostic Graphs
SG_inorg_N_trans_orig=subset(SG_inorg_N_trans_trt,soil_status =="L")
SG_soil_amm_trans_model_orig= lmer((ug_N_NH4_g_dry_soil_negto0)~precip*soil_root+(1|block), data= SG_inorg_N_trans_orig)## boundary (singular) fit: see ?isSingularqqPlot(resid(SG_soil_amm_trans_model_orig))
## 31 16
## 13 3hist(resid(SG_soil_amm_trans_model_orig))
shapiro.test(resid(SG_soil_amm_trans_model_orig))##
## Shapiro-Wilk normality test
##
## data: resid(SG_soil_amm_trans_model_orig)
## W = 0.9063, p-value = 0.1014Raw Statistical output
anova(SG_soil_amm_trans_model_orig, type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 0.006632 0.006632 1 12 0.4456 0.5171
## soil_root 0.001692 0.001692 1 12 0.1137 0.7418
## precip:soil_root 0.033168 0.033168 1 12 2.2285 0.1613emmeans(SG_soil_amm_trans_model_orig, pairwise~soil_root|precip)## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.1422 0.0618 12 0.00754 0.277
## L.R 0.0705 0.0546 12 -0.04833 0.189
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 0.0910 0.0618 12 -0.04363 0.226
## L.R 0.2045 0.0745 12 0.04209 0.367
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - L.R 0.0717 0.0824 9.12 0.869 0.4070
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - L.R -0.1135 0.0958 9.27 -1.184 0.2657
##
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-rogerFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 12 | 0.45 | 0.517 |
| 1 | 12 | 0.11 | 0.742 |
| 1 | 12 | 2.23 | 0.161 |
VWC Analyses
Diagnostic Graphs
SG_soil_VWC_model= lmer((percent_soil_moisture_dry_weight)~precip*soil_root*life_stage+(1|block), data= SG_inorg_N_trt)## boundary (singular) fit: see ?isSingularqqPlot(resid(SG_soil_VWC_model))
## [1] 20 33hist(resid(SG_soil_VWC_model))
shapiro.test(resid(SG_soil_VWC_model))##
## Shapiro-Wilk normality test
##
## data: resid(SG_soil_VWC_model)
## W = 0.96851, p-value = 0.3377#0.3377Raw Statistical output
anova(SG_soil_VWC_model, type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip 307.117 307.117 1 27 7.9808 0.008782 **
## soil_root 41.285 20.643 2 27 0.5364 0.590938
## life_stage 135.010 135.010 1 27 3.5084 0.071923 .
## precip:soil_root 22.074 11.037 2 27 0.2868 0.752914
## precip:life_stage 0.405 0.405 1 27 0.0105 0.919080
## soil_root:life_stage 13.084 6.542 2 27 0.1700 0.844555
## precip:soil_root:life_stage 3.642 1.821 2 27 0.0473 0.953857
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(SG_soil_VWC_model, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B 11.96 2.17 21.0 7.45 16.5
## S.B 8.86 2.20 23.1 4.32 13.4
## L.R 10.60 1.80 16.9 6.81 14.4
##
## Results are averaged over the levels of: precip, life_stage
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - S.B 3.10 3.06 25.0 1.011 0.5771
## L.B - L.R 1.35 2.74 23.6 0.493 0.8753
## S.B - L.R -1.74 2.82 24.7 -0.618 0.8117
##
## Results are averaged over the levels of: precip, life_stage
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimatesemmeans(SG_soil_VWC_model, pairwise~soil_root|precip)## NOTE: Results may be misleading due to involvement in interactions
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## precip = A:
## soil_root emmean SE df lower.CL upper.CL
## L.B 15.40 2.43 24.2 10.396 20.4
## S.B 11.00 2.68 25.7 5.489 16.5
## L.R 14.83 2.09 24.1 10.524 19.1
##
## precip = D:
## soil_root emmean SE df lower.CL upper.CL
## L.B 8.51 3.68 26.4 0.958 16.1
## S.B 6.73 3.59 26.5 -0.639 14.1
## L.R 6.37 2.90 26.4 0.408 12.3
##
## Results are averaged over the levels of: life_stage
## Degrees-of-freedom method: kenward-roger
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## precip = A:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 4.408 3.63 25.7 1.213 0.4564
## L.B - L.R 0.570 3.18 24.2 0.179 0.9825
## S.B - L.R -3.838 3.39 24.7 -1.133 0.5033
##
## precip = D:
## contrast estimate SE df t.ratio p.value
## L.B - S.B 1.784 5.20 26.9 0.343 0.9373
## L.B - L.R 2.136 4.53 24.1 0.472 0.8852
## S.B - L.R 0.352 4.66 26.5 0.075 0.9969
##
## Results are averaged over the levels of: life_stage
## Note: contrasts are still on the ( scale
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimatesFormatted Anova table
| Df | Resid.Df | F.value | P.value |
|---|---|---|---|
| 1 | 27 | 7.98 | 0.009 |
| 2 | 27 | 0.54 | 0.591 |
| 1 | 27 | 3.51 | 0.072 |
| 2 | 27 | 0.29 | 0.753 |
| 1 | 27 | 0.01 | 0.919 |
| 2 | 27 | 0.17 | 0.845 |
| 2 | 27 | 0.05 | 0.954 |
Start Soil nitrogen
Soil nitrate
Diagnostic Graphs
SG_start_inorg_N$soil_root=with(SG_start_inorg_N, interaction(soil_status,root_association))
SG_Soil_nit_start= lm((ug_N_NO3_g_dry_soil)~soil_root+block, data= SG_start_inorg_N)
qqPlot(resid(SG_Soil_nit_start))
## [1] 14 7hist(resid(SG_Soil_nit_start))
shapiro.test(resid(SG_Soil_nit_start))##
## Shapiro-Wilk normality test
##
## data: resid(SG_Soil_nit_start)
## W = 0.94653, p-value = 0.4716Raw Statistical output
Anova(SG_Soil_nit_start,type = 3)## Anova Table (Type III tests)
##
## Response: (ug_N_NO3_g_dry_soil)
## Sum Sq Df F value Pr(>F)
## (Intercept) 124.752 1 49.7581 2.116e-05 ***
## soil_root 3.977 2 0.7931 0.4767
## block 3.893 1 1.5529 0.2386
## Residuals 27.579 11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Formatted Anova table
| Df | F.value | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 49.76 | 0.000 |
| soil_root | 2 | 0.79 | 0.477 |
| block | 1 | 1.55 | 0.239 |
| Residuals | 11 | NA | NA |
Graph for publication 
Presence Start SOil nitrate
Diagnostic Graphs
SG_start_inorg_N_pres=subset(SG_start_inorg_N, root_association =="B")
SG_Soil_nit_start_pres= lm((ug_N_NO3_g_dry_soil)~soil_root+block, data= SG_start_inorg_N_pres)
qqPlot(resid(SG_Soil_nit_start_pres))
## 14 1
## 9 1hist(resid(SG_Soil_nit_start_pres))
shapiro.test(resid(SG_Soil_nit_start_pres))##
## Shapiro-Wilk normality test
##
## data: resid(SG_Soil_nit_start_pres)
## W = 0.93286, p-value = 0.4766Raw Statistical output
Anova(SG_Soil_nit_start_pres,type = 3)## Anova Table (Type III tests)
##
## Response: (ug_N_NO3_g_dry_soil)
## Sum Sq Df F value Pr(>F)
## (Intercept) 75.236 1 32.3655 0.0007437 ***
## soil_root 2.846 1 1.2242 0.3051005
## block 5.728 1 2.4643 0.1604494
## Residuals 16.272 7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Formatted Anova table
| Df | F.value | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 32.37 | 0.001 |
| soil_root | 1 | 1.22 | 0.305 |
| block | 1 | 2.46 | 0.160 |
| Residuals | 7 | NA | NA |
Origin Start SOil nitrate
Diagnostic Graphs
SG_start_inorg_N_orig=subset(SG_start_inorg_N, soil_status =="L")
SG_Soil_nit_start_orig= lm((ug_N_NO3_g_dry_soil)^3~soil_root+block, data= SG_start_inorg_N_orig)
qqPlot(resid(SG_Soil_nit_start_orig))
## [1] 3 7hist(resid(SG_Soil_nit_start_orig))
shapiro.test(resid(SG_Soil_nit_start_orig))##
## Shapiro-Wilk normality test
##
## data: resid(SG_Soil_nit_start_orig)
## W = 0.82833, p-value = 0.03194boxCox(SG_Soil_nit_start_orig)
Raw Statistical output
Anova(SG_Soil_nit_start_orig,type = 3)## Anova Table (Type III tests)
##
## Response: (ug_N_NO3_g_dry_soil)^3
## Sum Sq Df F value Pr(>F)
## (Intercept) 496747 1 7.4104 0.02967 *
## soil_root 104794 1 1.5633 0.25136
## block 2696 1 0.0402 0.84675
## Residuals 469239 7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Formatted Anova table
| Df | F.value | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 7.41 | 0.030 |
| soil_root | 1 | 1.56 | 0.251 |
| block | 1 | 0.04 | 0.847 |
| Residuals | 7 | NA | NA |
Soil ammonium
Diagnostic Graphs
SG_start_inorg_N$soil_root=with(SG_start_inorg_N, interaction(soil_status,root_association))
SG_Soil_amm_start= lm(log(ug_N_NH4_g_dry_soil)~soil_root+block, data= SG_start_inorg_N)
qqPlot(resid(SG_Soil_amm_start))
## [1] 4 10hist(resid(SG_Soil_amm_start))
shapiro.test(resid(SG_Soil_amm_start))##
## Shapiro-Wilk normality test
##
## data: resid(SG_Soil_amm_start)
## W = 0.93105, p-value = 0.2829boxCox(SG_Soil_amm_start)
Raw Statistical output
Anova(SG_Soil_amm_start,type = 3)## Anova Table (Type III tests)
##
## Response: log(ug_N_NH4_g_dry_soil)
## Sum Sq Df F value Pr(>F)
## (Intercept) 9.9251 1 98.8744 7.823e-07 ***
## soil_root 12.6065 2 62.7930 9.615e-07 ***
## block 0.0374 1 0.3728 0.5539
## Residuals 1.1042 11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Formatted Anova table
| Df | F.value | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 98.87 | 0.000 |
| soil_root | 2 | 62.79 | 0.000 |
| block | 1 | 0.37 | 0.554 |
| Residuals | 11 | NA | NA |
Graph for publication 
Presence Start SOil Ammonium
Diagnostic Graphs
SG_start_inorg_N_pres=subset(SG_start_inorg_N, root_association =="B")
SG_Soil_amm_start_pres= lm((ug_N_NH4_g_dry_soil)~soil_root+block, data= SG_start_inorg_N_pres)
qqPlot(resid(SG_Soil_amm_start_pres))
## 14 15
## 9 10hist(resid(SG_Soil_amm_start_pres))
shapiro.test(resid(SG_Soil_amm_start_pres))##
## Shapiro-Wilk normality test
##
## data: resid(SG_Soil_amm_start_pres)
## W = 0.87313, p-value = 0.1087boxCox(SG_Soil_amm_start_pres)
Raw Statistical output
Anova(SG_Soil_amm_start_pres,type = 3)## Anova Table (Type III tests)
##
## Response: (ug_N_NH4_g_dry_soil)
## Sum Sq Df F value Pr(>F)
## (Intercept) 247.50 1 9.3004 0.0185923 *
## soil_root 1423.78 1 53.5011 0.0001608 ***
## block 44.24 1 1.6626 0.2382233
## Residuals 186.29 7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Formatted Anova table
| Df | F.value | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 9.30 | 0.019 |
| soil_root | 1 | 53.50 | 0.000 |
| block | 1 | 1.66 | 0.238 |
| Residuals | 7 | NA | NA |
Origin Start SOil Ammonium
Diagnostic Graphs
SG_start_inorg_N_orig=subset(SG_start_inorg_N, soil_status =="L")
SG_Soil_amm_start_orig= lm((ug_N_NH4_g_dry_soil)^-1~soil_root+block, data= SG_start_inorg_N_orig)
qqPlot(resid(SG_Soil_amm_start_orig))
## [1] 4 10hist(resid(SG_Soil_amm_start_orig))
shapiro.test(resid(SG_Soil_amm_start_orig))##
## Shapiro-Wilk normality test
##
## data: resid(SG_Soil_amm_start_orig)
## W = 0.96944, p-value = 0.8856boxCox(SG_Soil_amm_start_orig)
Raw Statistical output
Anova(SG_Soil_amm_start_orig,type = 3)## Anova Table (Type III tests)
##
## Response: (ug_N_NH4_g_dry_soil)^-1
## Sum Sq Df F value Pr(>F)
## (Intercept) 0.128490 1 18.228 0.003704 **
## soil_root 0.000268 1 0.038 0.850978
## block 0.000000 1 0.000 0.996817
## Residuals 0.049344 7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Formatted Anova table
| Df | F.value | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 18.23 | 0.004 |
| soil_root | 1 | 0.04 | 0.851 |
| block | 1 | 0.00 | 0.997 |
| Residuals | 7 | NA | NA |
Microbial Biomass
Soil ammonium
Diagnostic Graphs
SG_Soil_DOC_mod= lm(log(unfumigated_DOC_concentration_ugC_gsoil)~soil_root+block, data= SG_MBC_DOC_trt)
qqPlot(resid(SG_Soil_DOC_mod))
## [1] 6 2hist(resid(SG_Soil_DOC_mod))
shapiro.test(resid(SG_Soil_DOC_mod))##
## Shapiro-Wilk normality test
##
## data: resid(SG_Soil_DOC_mod)
## W = 0.9533, p-value = 0.6131boxCox(SG_Soil_DOC_mod)
Raw Statistical output
Anova(SG_Soil_DOC_mod,type = 3)## Anova Table (Type III tests)
##
## Response: log(unfumigated_DOC_concentration_ugC_gsoil)
## Sum Sq Df F value Pr(>F)
## (Intercept) 16.5452 1 38.7139 9.857e-05 ***
## soil_root 2.2287 2 2.6074 0.1226
## block 0.0006 1 0.0014 0.9712
## Residuals 4.2737 10
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Formatted Anova table
| Df | F.value | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 38.71 | 0.000 |
| soil_root | 2 | 2.61 | 0.123 |
| block | 1 | 0.00 | 0.971 |
| Residuals | 10 | NA | NA |
Graph for publication 
Combined Seed and Seedling Analyses
Survival
Diagnostic Graphs
#data_SG_biomass_trans_surv_drought=subset(data_SG_biomass_trans_surv_trt, precip=="D")
SG_combin_surv_model= glmer(surv~soil_root*precip*life_stage+(1|block/life_stage), data= data_SG_biomass_surv_trt_comb, family = binomial)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 3 negative eigenvaluesqqPlot(resid(SG_combin_surv_model))
## 24 29
## 14 88hist(resid(SG_combin_surv_model))
shapiro.test(resid(SG_combin_surv_model))##
## Shapiro-Wilk normality test
##
## data: resid(SG_combin_surv_model)
## W = 0.83916, p-value = 1.729e-11Raw Statistical output
Anova(SG_combin_surv_model,type = 3)## Warning in vcov.merMod(mod, complete = FALSE): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX
## Warning in vcov.merMod(mod, complete = FALSE): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: surv
## Chisq Df Pr(>Chisq)
## (Intercept) 0 1 0.9949
## soil_root 0 2 1.0000
## precip 0 1 0.9960
## life_stage 0 1 0.9959
## soil_root:precip 0 2 1.0000
## soil_root:life_stage 0 2 1.0000
## precip:life_stage 0 1 0.9961
## soil_root:precip:life_stage 0 2 1.0000emmeans(SG_combin_surv_model, pairwise~soil_root)## Warning in vcov.merMod(object, correlation = FALSE): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B 6.67 1656 Inf -3239 3253
## S.B 5.37 1762 Inf -3449 3460
## L.R 6.44 1555 Inf -3042 3055
##
## Results are averaged over the levels of: precip, life_stage
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## L.B - S.B 1.297 2418 Inf 0.001 1.0000
## L.B - L.R 0.225 2272 Inf 0.000 1.0000
## S.B - L.R -1.071 2351 Inf 0.000 1.0000
##
## Results are averaged over the levels of: precip, life_stage
## Results are given on the log odds ratio (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesFormatted Anova table
## Warning in vcov.merMod(mod, complete = FALSE): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX
## Warning in vcov.merMod(mod, complete = FALSE): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX| Df | Chi.Sq | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 0 | 0.995 |
| soil_root | 2 | 0 | 1.000 |
| precip | 1 | 0 | 0.996 |
| life_stage | 1 | 0 | 0.996 |
| soil_root:precip | 2 | 0 | 1.000 |
| soil_root:life_stage | 2 | 0 | 1.000 |
| precip:life_stage | 1 | 0 | 0.996 |
| soil_root:precip:life_stage | 2 | 0 | 1.000 |
Focus in on Drought treatment
Diagnostic Graphs
data_SG_biomass_surv_trt_comb_drought=subset(data_SG_biomass_surv_trt_comb, precip=="D")
SG_combin_surv_drought_model= glmer(surv~soil_root*life_stage+(1|block/life_stage), data= data_SG_biomass_surv_trt_comb_drought, family = binomial)## boundary (singular) fit: see ?isSingularqqPlot(resid(SG_combin_surv_drought_model))
## 24 29
## 14 53hist(resid(SG_combin_surv_drought_model))
shapiro.test(resid(SG_combin_surv_drought_model))##
## Shapiro-Wilk normality test
##
## data: resid(SG_combin_surv_drought_model)
## W = 0.76715, p-value = 4.209e-09Raw Statistical output
Anova(SG_combin_surv_drought_model,type = 3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: surv
## Chisq Df Pr(>Chisq)
## (Intercept) 6.5558 1 0.01045 *
## soil_root 6.1744 2 0.04563 *
## life_stage 0.1295 1 0.71900
## soil_root:life_stage 3.7279 2 0.15506
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(SG_combin_surv_drought_model, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B 2.1866 0.778 Inf 0.662 3.71
## S.B 0.0958 0.668 Inf -1.214 1.41
## L.R 1.7439 0.761 Inf 0.253 3.23
##
## Results are averaged over the levels of: life_stage
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## L.B - S.B 2.091 0.896 Inf 2.335 0.0511
## L.B - L.R 0.443 0.912 Inf 0.486 0.8781
## S.B - L.R -1.648 0.886 Inf -1.860 0.1504
##
## Results are averaged over the levels of: life_stage
## Results are given on the log odds ratio (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesFormatted Anova table
| Df | Chi.Sq | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 6.56 | 0.010 |
| soil_root | 2 | 6.17 | 0.046 |
| life_stage | 1 | 0.13 | 0.719 |
| soil_root:life_stage | 2 | 3.73 | 0.155 |
| ### Presence combined s | urviva | l | |
| Diagnostic Graphs |
data_SG_biomass_surv_trt_comb_drought_pres=subset(data_SG_biomass_surv_trt_comb_drought, root_association =="B")
SG_combin_surv_drought_pres_model= glmer(surv~soil_root*life_stage+(1|block/life_stage), data= data_SG_biomass_surv_trt_comb_drought_pres, family = binomial)## boundary (singular) fit: see ?isSingularqqPlot(resid(SG_combin_surv_drought_pres_model))
## 24 16
## 6 17hist(resid(SG_combin_surv_drought_pres_model))
shapiro.test(resid(SG_combin_surv_drought_pres_model))##
## Shapiro-Wilk normality test
##
## data: resid(SG_combin_surv_drought_pres_model)
## W = 0.74597, p-value = 1.487e-07Raw Statistical output
Anova(SG_combin_surv_drought_pres_model,type = 3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: surv
## Chisq Df Pr(>Chisq)
## (Intercept) 6.9271 1 0.00849 **
## soil_root 5.0619 1 0.02446 *
## life_stage 0.6846 1 0.40800
## soil_root:life_stage 1.4832 1 0.22328
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(SG_combin_surv_drought_pres_model, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B 2.035 0.650 Inf 0.761 3.31
## S.B 0.159 0.522 Inf -0.864 1.18
##
## Results are averaged over the levels of: life_stage
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## L.B - S.B 1.88 0.834 Inf 2.250 0.0245
##
## Results are averaged over the levels of: life_stage
## Results are given on the log odds ratio (not the response) scale.Formatted Anova table
| Df | Chi.Sq | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 6.93 | 0.008 |
| soil_root | 1 | 5.06 | 0.024 |
| life_stage | 1 | 0.68 | 0.408 |
| soil_root:life_stage | 1 | 1.48 | 0.223 |
Origin combined survival
Diagnostic Graphs
data_SG_biomass_surv_trt_comb_drought_orig=subset(data_SG_biomass_surv_trt_comb_drought, soil_status =="L")
SG_combin_surv_drought_orig_model= glmer(surv~soil_root*life_stage+(1|block/life_stage), data= data_SG_biomass_surv_trt_comb_drought_orig, family = binomial)
qqPlot(resid(SG_combin_surv_drought_orig_model))
## 29 24
## 47 14hist(resid(SG_combin_surv_drought_orig_model))
shapiro.test(resid(SG_combin_surv_drought_orig_model))##
## Shapiro-Wilk normality test
##
## data: resid(SG_combin_surv_drought_orig_model)
## W = 0.61185, p-value = 4.955e-10Raw Statistical output
Anova(SG_combin_surv_drought_orig_model,type = 3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: surv
## Chisq Df Pr(>Chisq)
## (Intercept) 10.0712 1 0.001506 **
## soil_root 0.4678 1 0.493979
## life_stage 0.3427 1 0.558290
## soil_root:life_stage 0.0938 1 0.759389
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(SG_combin_surv_drought_orig_model, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## soil_root emmean SE df asymp.LCL asymp.UCL
## L.B 2.23 0.813 Inf 0.637 3.82
## L.R 1.58 0.712 Inf 0.188 2.98
##
## Results are averaged over the levels of: life_stage
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## L.B - L.R 0.646 0.944 Inf 0.684 0.4940
##
## Results are averaged over the levels of: life_stage
## Results are given on the log odds ratio (not the response) scale.Formatted Anova table
| Df | Chi.Sq | P.value | |
|---|---|---|---|
| (Intercept) | 1 | 10.07 | 0.002 |
| soil_root | 1 | 0.47 | 0.494 |
| life_stage | 1 | 0.34 | 0.558 |
| soil_root:life_stage | 1 | 0.09 | 0.759 |
Biomass
Diagnostic Graphs
SG_combin_biomass_model= lmer(log(total_biomass+0.001)~soil_root*precip*life_stage+(1|block/life_stage), data= data_SG_biomass_trt_comb)## boundary (singular) fit: see ?isSingularqqPlot(resid(SG_combin_biomass_model))
## 29 41
## 74 86hist(resid(SG_combin_biomass_model))
shapiro.test(resid(SG_combin_biomass_model))##
## Shapiro-Wilk normality test
##
## data: resid(SG_combin_biomass_model)
## W = 0.97333, p-value = 0.009383Raw Statistical output
anova(SG_combin_biomass_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## soil_root 16.1994 8.0997 2 120.64 4.2800 0.016003 *
## precip 4.8969 4.8969 1 119.83 2.5876 0.110338
## life_stage 8.0818 8.0818 1 122.02 4.2705 0.040894 *
## soil_root:precip 3.1145 1.5573 2 121.19 0.8229 0.441607
## soil_root:life_stage 1.5159 0.7580 2 120.64 0.4005 0.670864
## precip:life_stage 12.5200 12.5200 1 119.83 6.6157 0.011330 *
## soil_root:precip:life_stage 26.2502 13.1251 2 121.19 6.9355 0.001407 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(SG_combin_biomass_model, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B -3.21 0.244 12.6 -3.74 -2.68
## S.B -2.21 0.357 39.6 -2.93 -1.49
## L.R -3.24 0.250 13.3 -3.78 -2.70
##
## Results are averaged over the levels of: precip, life_stage
## Degrees-of-freedom method: kenward-roger
## Results are given on the log(mu + 0.001) (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - S.B -1.0068 0.383 121 -2.627 0.0261
## L.B - L.R 0.0259 0.291 118 0.089 0.9956
## S.B - L.R 1.0327 0.394 120 2.622 0.0265
##
## Results are averaged over the levels of: precip, life_stage
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesemmeans(SG_combin_biomass_model, pairwise~life_stage)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## life_stage emmean SE df lower.CL upper.CL
## germinant -2.59 0.292 16.74 -3.21 -1.97
## seedling -3.18 0.198 4.93 -3.69 -2.67
##
## Results are averaged over the levels of: soil_root, precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log(mu + 0.001) (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## germinant - seedling 0.593 0.297 5.87 1.996 0.0940
##
## Results are averaged over the levels of: soil_root, precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.emmeans(SG_combin_biomass_model, pairwise~precip*life_stage)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## precip life_stage emmean SE df lower.CL upper.CL
## A germinant -2.73 0.322 26.6 -3.39 -2.07
## D germinant -2.45 0.437 57.6 -3.33 -1.58
## A seedling -2.59 0.245 11.6 -3.12 -2.05
## D seedling -3.78 0.245 11.6 -4.31 -3.24
##
## Results are averaged over the levels of: soil_root
## Degrees-of-freedom method: kenward-roger
## Results are given on the log(mu + 0.001) (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## A,germinant - D,germinant -0.275 0.497 119.8 -0.552 0.9458
## A,germinant - A,seedling -0.140 0.357 13.4 -0.391 0.9789
## A,germinant - D,seedling 1.052 0.357 13.4 2.943 0.0482
## D,germinant - A,seedling 0.135 0.463 28.7 0.291 0.9912
## D,germinant - D,seedling 1.327 0.463 28.7 2.862 0.0369
## A,seedling - D,seedling 1.192 0.290 115.7 4.109 0.0004
##
## Results are averaged over the levels of: soil_root
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimatesemmeans(SG_combin_biomass_model, pairwise~life_stage|soil_root|precip)## $emmeans
## soil_root = L.B, precip = A:
## life_stage emmean SE df lower.CL upper.CL
## germinant -2.657 0.464 74.8 -3.58 -1.733
## seedling -3.161 0.380 52.7 -3.92 -2.399
##
## soil_root = S.B, precip = A:
## life_stage emmean SE df lower.CL upper.CL
## germinant -3.218 0.642 109.2 -4.49 -1.947
## seedling -1.298 0.380 52.7 -2.06 -0.536
##
## soil_root = L.R, precip = A:
## life_stage emmean SE df lower.CL upper.CL
## germinant -2.304 0.424 64.2 -3.15 -1.457
## seedling -3.301 0.380 52.7 -4.06 -2.540
##
## soil_root = L.B, precip = D:
## life_stage emmean SE df lower.CL upper.CL
## germinant -3.412 0.485 82.6 -4.38 -2.448
## seedling -3.621 0.380 52.7 -4.38 -2.859
##
## soil_root = S.B, precip = D:
## life_stage emmean SE df lower.CL upper.CL
## germinant -0.642 1.058 102.9 -2.74 1.456
## seedling -3.666 0.380 52.7 -4.43 -2.904
##
## soil_root = L.R, precip = D:
## life_stage emmean SE df lower.CL upper.CL
## germinant -3.301 0.556 90.3 -4.41 -2.197
## seedling -4.048 0.380 52.7 -4.81 -3.287
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log(mu + 0.001) (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## soil_root = L.B, precip = A:
## contrast estimate SE df t.ratio p.value
## germinant - seedling 0.504 0.568 59.1 0.887 0.3786
##
## soil_root = S.B, precip = A:
## contrast estimate SE df t.ratio p.value
## germinant - seedling -1.920 0.721 86.3 -2.663 0.0092
##
## soil_root = L.R, precip = A:
## contrast estimate SE df t.ratio p.value
## germinant - seedling 0.997 0.537 53.4 1.858 0.0687
##
## soil_root = L.B, precip = D:
## contrast estimate SE df t.ratio p.value
## germinant - seedling 0.209 0.586 64.1 0.356 0.7229
##
## soil_root = S.B, precip = D:
## contrast estimate SE df t.ratio p.value
## germinant - seedling 3.024 1.108 89.6 2.729 0.0076
##
## soil_root = L.R, precip = D:
## contrast estimate SE df t.ratio p.value
## germinant - seedling 0.747 0.646 68.9 1.157 0.2513
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.Formatted Anova table
| Df | Resid.Df | F.value | P.value | |
|---|---|---|---|---|
| soil_root | 2 | 120.63 | 4.28 | 0.016 |
| precip | 1 | 119.83 | 2.59 | 0.110 |
| life_stage | 1 | 122.02 | 4.27 | 0.041 |
| soil_root:precip | 2 | 121.20 | 0.82 | 0.442 |
| soil_root:life_stage | 2 | 120.63 | 0.40 | 0.671 |
| precip:life_stage | 1 | 119.83 | 6.62 | 0.011 |
| soil_root:precip:life_stage | 2 | 121.20 | 6.94 | 0.001 |
| ### Presence Combined Biomass |
Diagnostic Graphs
data_SG_biomass_trt_comb_pres=subset(data_SG_biomass_trt_comb, root_association =="B")
SG_combin_biomass_pres_model= lmer(log(total_biomass+0.001)~soil_root*precip*life_stage+(1|block/life_stage), data= data_SG_biomass_trt_comb_pres)## boundary (singular) fit: see ?isSingularqqPlot(resid(SG_combin_biomass_pres_model))
## 41 36
## 52 47hist(resid(SG_combin_biomass_pres_model))
shapiro.test(resid(SG_combin_biomass_pres_model))##
## Shapiro-Wilk normality test
##
## data: resid(SG_combin_biomass_pres_model)
## W = 0.95636, p-value = 0.005474Raw Statistical output
anova(SG_combin_biomass_pres_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## soil_root 14.6111 14.6111 1 76.129 7.7962 0.0066170 **
## precip 0.6526 0.6526 1 76.407 0.3482 0.5568529
## life_stage 3.1065 3.1065 1 77.802 1.6576 0.2017495
## soil_root:precip 2.0195 2.0195 1 77.573 1.0776 0.3024683
## soil_root:life_stage 0.2408 0.2408 1 76.129 0.1285 0.7209959
## precip:life_stage 19.0950 19.0950 1 76.407 10.1887 0.0020518 **
## soil_root:precip:life_stage 24.0017 24.0017 1 77.573 12.8068 0.0005993 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(SG_combin_biomass_pres_model, pairwise~soil_root)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## soil_root emmean SE df lower.CL upper.CL
## L.B -3.22 0.235 8.46 -3.75 -2.68
## S.B -2.17 0.357 27.22 -2.91 -1.44
##
## Results are averaged over the levels of: precip, life_stage
## Degrees-of-freedom method: kenward-roger
## Results are given on the log(mu + 0.001) (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## L.B - S.B -1.04 0.387 76.3 -2.694 0.0087
##
## Results are averaged over the levels of: precip, life_stage
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.emmeans(SG_combin_biomass_pres_model, pairwise~precip*life_stage)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## precip life_stage emmean SE df lower.CL upper.CL
## A germinant -2.94 0.403 37.1 -3.76 -2.122
## D germinant -1.97 0.606 47.9 -3.18 -0.748
## A seedling -2.23 0.275 13.5 -2.82 -1.637
## D seedling -3.64 0.275 13.5 -4.24 -3.051
##
## Results are averaged over the levels of: soil_root
## Degrees-of-freedom method: kenward-roger
## Results are given on the log(mu + 0.001) (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## A,germinant - D,germinant -0.973 0.694 76.7 -1.402 0.5021
## A,germinant - A,seedling -0.709 0.460 14.4 -1.542 0.4399
## A,germinant - D,seedling 0.705 0.460 14.4 1.531 0.4454
## D,germinant - A,seedling 0.263 0.645 24.7 0.408 0.9765
## D,germinant - D,seedling 1.677 0.645 24.7 2.601 0.0689
## A,seedling - D,seedling 1.414 0.353 71.1 4.000 0.0009
##
## Results are averaged over the levels of: soil_root
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimatesemmeans(SG_combin_biomass_pres_model, pairwise~life_stage|soil_root|precip)## $emmeans
## soil_root = L.B, precip = A:
## life_stage emmean SE df lower.CL upper.CL
## germinant -2.661 0.461 49.7 -3.59 -1.734
## seedling -3.161 0.372 36.6 -3.92 -2.407
##
## soil_root = S.B, precip = A:
## life_stage emmean SE df lower.CL upper.CL
## germinant -3.217 0.642 70.8 -4.50 -1.938
## seedling -1.298 0.372 36.6 -2.05 -0.544
##
## soil_root = L.B, precip = D:
## life_stage emmean SE df lower.CL upper.CL
## germinant -3.422 0.482 55.1 -4.39 -2.456
## seedling -3.621 0.372 36.6 -4.37 -2.867
##
## soil_root = S.B, precip = D:
## life_stage emmean SE df lower.CL upper.CL
## germinant -0.511 1.094 62.9 -2.70 1.675
## seedling -3.666 0.372 36.6 -4.42 -2.912
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log(mu + 0.001) (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## soil_root = L.B, precip = A:
## contrast estimate SE df t.ratio p.value
## germinant - seedling 0.501 0.570 28.5 0.879 0.3867
##
## soil_root = S.B, precip = A:
## contrast estimate SE df t.ratio p.value
## germinant - seedling -1.919 0.723 46.7 -2.653 0.0109
##
## soil_root = L.B, precip = D:
## contrast estimate SE df t.ratio p.value
## germinant - seedling 0.200 0.586 31.5 0.341 0.7357
##
## soil_root = S.B, precip = D:
## contrast estimate SE df t.ratio p.value
## germinant - seedling 3.155 1.144 52.0 2.759 0.0080
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.Formatted Anova table
| Df | Resid.Df | F.value | P.value | |
|---|---|---|---|---|
| soil_root | 1 | 76.13 | 7.80 | 0.007 |
| precip | 1 | 76.41 | 0.35 | 0.557 |
| life_stage | 1 | 77.80 | 1.66 | 0.202 |
| soil_root:precip | 1 | 77.57 | 1.08 | 0.302 |
| soil_root:life_stage | 1 | 76.13 | 0.13 | 0.721 |
| precip:life_stage | 1 | 76.41 | 10.19 | 0.002 |
| soil_root:precip:life_stage | 1 | 77.57 | 12.81 | 0.001 |
Origin Combined Biomass
Diagnostic Graphs
data_SG_biomass_trt_comb_orig=subset(data_SG_biomass_trt_comb, soil_status =="L")
SG_combin_biomass_orig_model= lmer(log(total_biomass+0.001)~soil_root*precip*life_stage+(1|block/life_stage), data= data_SG_biomass_trt_comb_orig)
qqPlot(resid(SG_combin_biomass_orig_model))
## 29 16
## 67 54hist(resid(SG_combin_biomass_orig_model))
shapiro.test(resid(SG_combin_biomass_orig_model))##
## Shapiro-Wilk normality test
##
## data: resid(SG_combin_biomass_orig_model)
## W = 0.98373, p-value = 0.2687Raw Statistical output
anova(SG_combin_biomass_orig_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## soil_root 0.0170 0.0170 1 85.439 0.0100 0.920743
## precip 12.2652 12.2652 1 87.205 7.1861 0.008784 **
## life_stage 6.5403 6.5403 1 4.368 3.8319 0.115906
## soil_root:precip 0.4565 0.4565 1 84.367 0.2674 0.606410
## soil_root:life_stage 1.4816 1.4816 1 85.439 0.8681 0.354118
## precip:life_stage 0.4221 0.4221 1 87.205 0.2473 0.620225
## soil_root:precip:life_stage 0.0001 0.0001 1 84.367 0.0000 0.995601
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(SG_combin_biomass_orig_model, pairwise~precip)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## precip emmean SE df lower.CL upper.CL
## A -2.85 0.224 8.49 -3.36 -2.34
## D -3.59 0.243 11.13 -4.12 -3.05
##
## Results are averaged over the levels of: soil_root, life_stage
## Degrees-of-freedom method: kenward-roger
## Results are given on the log(mu + 0.001) (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## A - D 0.741 0.281 86.7 2.636 0.0099
##
## Results are averaged over the levels of: soil_root, life_stage
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.Formatted Anova table
| Df | Resid.Df | F.value | P.value | |
|---|---|---|---|---|
| soil_root | 1 | 85.44 | 0.01 | 0.921 |
| precip | 1 | 87.20 | 7.19 | 0.009 |
| life_stage | 1 | 4.37 | 3.83 | 0.116 |
| soil_root:precip | 1 | 84.37 | 0.27 | 0.606 |
| soil_root:life_stage | 1 | 85.44 | 0.87 | 0.354 |
| precip:life_stage | 1 | 87.20 | 0.25 | 0.620 |
| soil_root:precip:life_stage | 1 | 84.37 | 0.00 | 0.996 |
Root:shoot ratio
Diagnostic Graphs
SG_combin_root_shoot_model= lmer(log(root_shoot+0.001)~soil_root*precip*life_stage+(1|block/life_stage), data= data_SG_root_shoot_trt_comb)## boundary (singular) fit: see ?isSingularqqPlot(resid(SG_combin_root_shoot_model))
## 531 45
## 88 80hist(resid(SG_combin_root_shoot_model))
shapiro.test(resid(SG_combin_root_shoot_model))##
## Shapiro-Wilk normality test
##
## data: resid(SG_combin_root_shoot_model)
## W = 0.97959, p-value = 0.05557Raw Statistical output
anova(SG_combin_root_shoot_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## soil_root 1.231 0.615 2 113 0.4566 0.63462
## precip 8.680 8.680 1 113 6.4396 0.01252 *
## life_stage 31.625 31.625 1 113 23.4632 4.08e-06 ***
## soil_root:precip 1.711 0.856 2 113 0.6349 0.53189
## soil_root:life_stage 1.276 0.638 2 113 0.4735 0.62405
## precip:life_stage 1.687 1.687 1 113 1.2517 0.26560
## soil_root:precip:life_stage 6.372 3.186 2 113 2.3637 0.09871 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(SG_combin_root_shoot_model, pairwise~life_stage)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## life_stage emmean SE df lower.CL upper.CL
## germinant 2.34 0.217 21.53 1.890 2.79
## seedling 1.16 0.132 5.06 0.817 1.50
##
## Results are averaged over the levels of: soil_root, precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log(mu + 0.001) (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## germinant - seedling 1.18 0.254 6.13 4.661 0.0033
##
## Results are averaged over the levels of: soil_root, precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.emmeans(SG_combin_root_shoot_model, pairwise~life_stage|soil_root|precip)## $emmeans
## soil_root = L.B, precip = A:
## life_stage emmean SE df lower.CL upper.CL
## germinant 2.058 0.374 92.1 1.316 2.80
## seedling 0.815 0.300 78.7 0.218 1.41
##
## soil_root = S.B, precip = A:
## life_stage emmean SE df lower.CL upper.CL
## germinant 1.523 0.528 109.8 0.477 2.57
## seedling 1.055 0.300 78.7 0.459 1.65
##
## soil_root = L.R, precip = A:
## life_stage emmean SE df lower.CL upper.CL
## germinant 2.099 0.339 86.2 1.426 2.77
## seedling 1.078 0.300 78.7 0.481 1.67
##
## soil_root = L.B, precip = D:
## life_stage emmean SE df lower.CL upper.CL
## germinant 2.106 0.392 98.2 1.327 2.88
## seedling 1.439 0.323 85.6 0.796 2.08
##
## soil_root = S.B, precip = D:
## life_stage emmean SE df lower.CL upper.CL
## germinant 3.667 0.881 100.4 1.918 5.42
## seedling 1.010 0.353 90.9 0.309 1.71
##
## soil_root = L.R, precip = D:
## life_stage emmean SE df lower.CL upper.CL
## germinant 2.588 0.454 97.6 1.687 3.49
## seedling 1.539 0.359 82.8 0.824 2.25
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log(mu + 0.001) (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## soil_root = L.B, precip = A:
## contrast estimate SE df t.ratio p.value
## germinant - seedling 1.243 0.479 58.3 2.595 0.0120
##
## soil_root = S.B, precip = A:
## contrast estimate SE df t.ratio p.value
## germinant - seedling 0.468 0.607 84.7 0.770 0.4432
##
## soil_root = L.R, precip = A:
## contrast estimate SE df t.ratio p.value
## germinant - seedling 1.021 0.452 52.6 2.259 0.0280
##
## soil_root = L.B, precip = D:
## contrast estimate SE df t.ratio p.value
## germinant - seedling 0.667 0.508 63.4 1.312 0.1942
##
## soil_root = S.B, precip = D:
## contrast estimate SE df t.ratio p.value
## germinant - seedling 2.657 0.950 91.5 2.798 0.0063
##
## soil_root = L.R, precip = D:
## contrast estimate SE df t.ratio p.value
## germinant - seedling 1.049 0.579 75.6 1.811 0.0741
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.Formatted Anova table
| Df | Resid.Df | F.value | P.value | |
|---|---|---|---|---|
| soil_root | 2 | 113 | 0.46 | 0.635 |
| precip | 1 | 113 | 6.44 | 0.013 |
| life_stage | 1 | 113 | 23.46 | 0.000 |
| soil_root:precip | 2 | 113 | 0.63 | 0.532 |
| soil_root:life_stage | 2 | 113 | 0.47 | 0.624 |
| precip:life_stage | 1 | 113 | 1.25 | 0.266 |
| soil_root:precip:life_stage | 2 | 113 | 2.36 | 0.099 |
| ### Presence Combined Root:Sho | ot |
Diagnostic Graphs
data_SG_root_shoot_trt_comb_pres=subset(data_SG_root_shoot_trt_comb, root_association =="B")
SG_combin_root_shoot_pres_model= lmer(sqrt(root_shoot)~soil_root*precip*life_stage+(1|block/life_stage), data= data_SG_root_shoot_trt_comb_pres)## boundary (singular) fit: see ?isSingularqqPlot(resid(SG_combin_root_shoot_pres_model))
## 251 20
## 35 2hist(resid(SG_combin_root_shoot_pres_model))
shapiro.test(resid(SG_combin_root_shoot_pres_model))##
## Shapiro-Wilk normality test
##
## data: resid(SG_combin_root_shoot_pres_model)
## W = 0.96226, p-value = 0.01852Raw Statistical output
anova(SG_combin_root_shoot_pres_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## soil_root 3.820 3.820 1 72 3.0989 0.0825929 .
## precip 19.930 19.930 1 72 16.1697 0.0001411 ***
## life_stage 33.827 33.827 1 72 27.4445 1.542e-06 ***
## soil_root:precip 7.796 7.796 1 72 6.3251 0.0141377 *
## soil_root:life_stage 5.247 5.247 1 72 4.2571 0.0426929 *
## precip:life_stage 9.546 9.546 1 72 7.7446 0.0068752 **
## soil_root:precip:life_stage 20.164 20.164 1 72 16.3594 0.0001300 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(SG_combin_root_shoot_pres_model, pairwise~precip*life_stage)## NOTE: Results may be misleading due to involvement in interactions## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## precip life_stage emmean SE df lower.CL upper.CL
## A germinant 2.59 0.311 42.1 1.97 3.22
## D germinant 4.67 0.481 47.4 3.71 5.64
## A seedling 1.84 0.203 14.9 1.41 2.27
## D seedling 2.22 0.230 20.8 1.74 2.70
##
## Results are averaged over the levels of: soil_root
## Degrees-of-freedom method: kenward-roger
## Results are given on the sqrt (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## A,germinant - D,germinant -2.079 0.563 72.0 -3.696 0.0023
## A,germinant - A,seedling 0.750 0.371 14.8 2.022 0.2242
## A,germinant - D,seedling 0.372 0.387 15.4 0.962 0.7723
## D,germinant - A,seedling 2.830 0.522 26.4 5.425 0.0001
## D,germinant - D,seedling 2.451 0.533 25.5 4.601 0.0005
## A,seedling - D,seedling -0.378 0.307 66.9 -1.234 0.6075
##
## Results are averaged over the levels of: soil_root
## Note: contrasts are still on the sqrt scale
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 4 estimatesemmeans(SG_combin_root_shoot_pres_model, pairwise~life_stage|soil_root|precip)## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $emmeans
## soil_root = L.B, precip = A:
## life_stage emmean SE df lower.CL upper.CL
## germinant 3.01 0.361 53.5 2.29 3.74
## seedling 1.66 0.287 43.2 1.08 2.23
##
## soil_root = S.B, precip = A:
## life_stage emmean SE df lower.CL upper.CL
## germinant 2.18 0.509 69.0 1.16 3.19
## seedling 2.03 0.287 43.2 1.45 2.61
##
## soil_root = L.B, precip = D:
## life_stage emmean SE df lower.CL upper.CL
## germinant 3.09 0.378 58.5 2.33 3.84
## seedling 2.50 0.310 48.3 1.88 3.12
##
## soil_root = S.B, precip = D:
## life_stage emmean SE df lower.CL upper.CL
## germinant 6.26 0.881 60.4 4.50 8.02
## seedling 1.94 0.339 52.5 1.26 2.62
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the sqrt (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## soil_root = L.B, precip = A:
## contrast estimate SE df t.ratio p.value
## germinant - seedling 1.356 0.461 28.7 2.941 0.0064
##
## soil_root = S.B, precip = A:
## contrast estimate SE df t.ratio p.value
## germinant - seedling 0.145 0.584 46.8 0.248 0.8051
##
## soil_root = L.B, precip = D:
## contrast estimate SE df t.ratio p.value
## germinant - seedling 0.585 0.489 31.9 1.197 0.2401
##
## soil_root = S.B, precip = D:
## contrast estimate SE df t.ratio p.value
## germinant - seedling 4.318 0.944 52.6 4.575 <.0001
##
## Note: contrasts are still on the sqrt scale
## Degrees-of-freedom method: kenward-rogerFormatted Anova table
| Df | Resid.Df | F.value | P.value | |
|---|---|---|---|---|
| soil_root | 1 | 72 | 3.10 | 0.083 |
| precip | 1 | 72 | 16.17 | 0.000 |
| life_stage | 1 | 72 | 27.44 | 0.000 |
| soil_root:precip | 1 | 72 | 6.33 | 0.014 |
| soil_root:life_stage | 1 | 72 | 4.26 | 0.043 |
| precip:life_stage | 1 | 72 | 7.74 | 0.007 |
| soil_root:precip:life_stage | 1 | 72 | 16.36 | 0.000 |
Origin Combined Root:Shoot
Diagnostic Graphs
data_SG_root_shoot_trt_comb_orig=subset(data_SG_root_shoot_trt_comb, soil_status =="L")
SG_combin_root_shoot_orig_model= lmer(log(root_shoot+0.001)~soil_root*precip*life_stage+(1|block/life_stage), data= data_SG_root_shoot_trt_comb_orig)## boundary (singular) fit: see ?isSingularqqPlot(resid(SG_combin_root_shoot_orig_model))
## 531 51
## 70 20hist(resid(SG_combin_root_shoot_orig_model))
shapiro.test(resid(SG_combin_root_shoot_orig_model))##
## Shapiro-Wilk normality test
##
## data: resid(SG_combin_root_shoot_orig_model)
## W = 0.97332, p-value = 0.0558Raw Statistical output
anova(SG_combin_root_shoot_orig_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## soil_root 1.0674 1.0674 1 84 0.8207 0.3675748
## precip 3.5647 3.5647 1 84 2.7406 0.1015573
## life_stage 21.4534 21.4534 1 84 16.4939 0.0001091 ***
## soil_root:precip 0.1050 0.1050 1 84 0.0807 0.7770479
## soil_root:life_stage 0.0350 0.0350 1 84 0.0269 0.8700175
## precip:life_stage 0.4067 0.4067 1 84 0.3127 0.5775090
## soil_root:precip:life_stage 0.4929 0.4929 1 84 0.3790 0.5398262
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(SG_combin_root_shoot_orig_model, pairwise~life_stage)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## life_stage emmean SE df lower.CL upper.CL
## germinant 2.21 0.196 10.4 1.778 2.65
## seedling 1.22 0.158 5.8 0.827 1.61
##
## Results are averaged over the levels of: soil_root, precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log(mu + 0.001) (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## germinant - seedling 0.995 0.252 4.03 3.949 0.0166
##
## Results are averaged over the levels of: soil_root, precip
## Degrees-of-freedom method: kenward-roger
## Results are given on the log (not the response) scale.Formatted Anova table
| Df | Resid.Df | F.value | P.value | |
|---|---|---|---|---|
| soil_root | 1 | 84 | 0.82 | 0.368 |
| precip | 1 | 84 | 2.74 | 0.102 |
| life_stage | 1 | 84 | 16.49 | 0.000 |
| soil_root:precip | 1 | 84 | 0.08 | 0.777 |
| soil_root:life_stage | 1 | 84 | 0.03 | 0.870 |
| precip:life_stage | 1 | 84 | 0.31 | 0.578 |
| soil_root:precip:life_stage | 1 | 84 | 0.38 | 0.540 |